Level 2: Core functionality and level_2 script wrappers added

source/core/aac_coder.py

@@ -30,6 +30,7 @@ import soundfile as sf
 from core.aac_configuration import WIN_TYPE
 from core.aac_filterbank import aac_filter_bank
 from core.aac_ssc import aac_SSC
+from core.aac_tns import aac_tns
 from core.aac_types import *
 
 
@@ -144,8 +145,8 @@ def aac_coder_1(filename_in: Union[str, Path]) -> AACSeq1:
     AACSeq1
         List of encoded frames (Level 1 schema).
     """
-    x, fs = aac_read_wav_stereo_48k(filename_in)
+    x, _ = aac_read_wav_stereo_48k(filename_in)
-    _ = fs  # kept for clarity; The assignment assumes 48 kHz
+    # The assignment assumes 48 kHz
 
     hop = 1024
     win = 2048
@@ -196,3 +197,88 @@ def aac_coder_1(filename_in: Union[str, Path]) -> AACSeq1:
         prev_frame_type = frame_type
 
     return aac_seq
+
+
+def aac_coder_2(filename_in: Union[str, Path]) -> AACSeq2:
+    """
+    Level-2 AAC encoder (Level 1 + TNS).
+
+    Parameters
+    ----------
+    filename_in : Union[str, Path]
+        Input WAV filename (stereo, 48 kHz).
+
+    Returns
+    -------
+    AACSeq2
+        Encoded AAC sequence (Level 2 payload schema).
+        For each frame i:
+        - "frame_type": FrameType
+        - "win_type": WinType
+        - "chl"/"chr":
+            - "frame_F": FrameChannelF (after TNS)
+            - "tns_coeffs": TnsCoeffs
+    """
+    filename_in = Path(filename_in)
+
+    x, _ = aac_read_wav_stereo_48k(filename_in)
+    # The assignment assumes 48 kHz
+
+    hop = 1024
+    win = 2048
+
+    pad_pre = np.zeros((hop, 2), dtype=np.float64)
+    pad_post = np.zeros((hop, 2), dtype=np.float64)
+    x_pad = np.vstack([pad_pre, x, pad_post])
+
+    K = int((x_pad.shape[0] - win) // hop + 1)
+    if K <= 0:
+        raise ValueError("Input too short for framing.")
+
+    aac_seq: AACSeq2 = []
+    prev_frame_type: FrameType = "OLS"
+
+    for i in range(K):
+        start = i * hop
+
+        frame_t: FrameT = x_pad[start : start + win, :]
+        if frame_t.shape != (win, 2):
+            raise ValueError("Internal framing error: frame_t has wrong shape.")
+
+        next_t = x_pad[start + hop : start + hop + win, :]
+        if next_t.shape[0] < win:
+            tail = np.zeros((win - next_t.shape[0], 2), dtype=np.float64)
+            next_t = np.vstack([next_t, tail])
+
+        frame_type = aac_SSC(frame_t, next_t, prev_frame_type)
+
+        # Level 1 analysis (packed stereo container)
+        frame_f_stereo = aac_filter_bank(frame_t, frame_type, WIN_TYPE)
+
+        # Unpack to per-channel (as you already do in Level 1)
+        if frame_type == "ESH":
+            chl_f = np.empty((128, 8), dtype=np.float64)
+            chr_f = np.empty((128, 8), dtype=np.float64)
+            for j in range(8):
+                chl_f[:, j] = frame_f_stereo[:, 2 * j + 0]
+                chr_f[:, j] = frame_f_stereo[:, 2 * j + 1]
+        else:
+            chl_f = frame_f_stereo[:, 0:1].astype(np.float64, copy=False)
+            chr_f = frame_f_stereo[:, 1:2].astype(np.float64, copy=False)
+
+        # Level 2: apply TNS per channel
+        chl_f_tns, chl_tns_coeffs = aac_tns(chl_f, frame_type)
+        chr_f_tns, chr_tns_coeffs = aac_tns(chr_f, frame_type)
+
+        aac_seq.append(
+            {
+                "frame_type": frame_type,
+                "win_type": WIN_TYPE,
+                "chl": {"frame_F": chl_f_tns, "tns_coeffs": chl_tns_coeffs},
+                "chr": {"frame_F": chr_f_tns, "tns_coeffs": chr_tns_coeffs},
+            }
+        )
+
+        prev_frame_type = frame_type
+
+    return aac_seq

source/core/aac_configuration.py

@@ -17,6 +17,15 @@ from __future__ import annotations
 # Imports
 from core.aac_types import WinType
 
+# Filterbank
+# ------------------------------------------------------------
 # Window type
 # Options: "SIN", "KBD"
 WIN_TYPE: WinType = "SIN"
+
+
+# TNS
+# ------------------------------------------------------------
+PRED_ORDER = 4
+QUANT_STEP = 0.1
+QUANT_MAX = 0.7  # 4-bit symmetric with step 0.1 -> clamp to [-0.7, +0.7]

source/core/aac_decoder.py

@@ -28,6 +28,7 @@ from typing import Union
 import soundfile as sf
 
 from core.aac_filterbank import aac_i_filter_bank
+from core.aac_tns import aac_i_tns
 from core.aac_types import *
 
 
@@ -164,3 +165,93 @@ def aac_decoder_1(aac_seq_1: AACSeq1, filename_out: Union[str, Path]) -> StereoS
     sf.write(str(filename_out), y, 48000)
 
     return y
+
+
+def aac_decoder_2(aac_seq_2: AACSeq2, filename_out: Union[str, Path]) -> StereoSignal:
+    """
+    Level-2 AAC decoder (inverse of aac_coder_2).
+
+    Behavior matches Level 1 decoder pipeline, with additional iTNS stage:
+    - Per frame/channel: inverse TNS using stored coefficients
+    - Re-pack to stereo frame_F
+    - IMDCT + windowing
+    - Overlap-add over frames
+    - Remove Level-1 padding (hop samples start/end)
+    - Write output WAV (48 kHz)
+
+    Parameters
+    ----------
+    aac_seq_2 : AACSeq2
+        Encoded sequence as produced by aac_coder_2().
+    filename_out : Union[str, Path]
+        Output WAV filename.
+
+    Returns
+    -------
+    StereoSignal
+        Decoded audio samples (time-domain), stereo, shape (N, 2), dtype float64.
+    """
+    filename_out = Path(filename_out)
+
+    hop = 1024
+    win = 2048
+    K = len(aac_seq_2)
+
+    if K <= 0:
+        raise ValueError("aac_seq_2 must contain at least one frame.")
+
+    n_pad = (K - 1) * hop + win
+    y_pad = np.zeros((n_pad, 2), dtype=np.float64)
+
+    for i, fr in enumerate(aac_seq_2):
+        frame_type: FrameType = fr["frame_type"]
+        win_type: WinType = fr["win_type"]
+
+        chl_f_tns = np.asarray(fr["chl"]["frame_F"], dtype=np.float64)
+        chr_f_tns = np.asarray(fr["chr"]["frame_F"], dtype=np.float64)
+
+        chl_coeffs = np.asarray(fr["chl"]["tns_coeffs"], dtype=np.float64)
+        chr_coeffs = np.asarray(fr["chr"]["tns_coeffs"], dtype=np.float64)
+
+        # Inverse TNS per channel
+        chl_f = aac_i_tns(chl_f_tns, frame_type, chl_coeffs)
+        chr_f = aac_i_tns(chr_f_tns, frame_type, chr_coeffs)
+
+        # Re-pack to the stereo container expected by aac_i_filter_bank
+        if frame_type == "ESH":
+            if chl_f.shape != (128, 8) or chr_f.shape != (128, 8):
+                raise ValueError("ESH channel frame_F must have shape (128, 8).")
+
+            frame_f: FrameF = np.empty((128, 16), dtype=np.float64)
+            for j in range(8):
+                frame_f[:, 2 * j + 0] = chl_f[:, j]
+                frame_f[:, 2 * j + 1] = chr_f[:, j]
+        else:
+            # Accept either (1024,1) or (1024,) from your internal convention.
+            if chl_f.shape == (1024,):
+                chl_col = chl_f.reshape(1024, 1)
+            elif chl_f.shape == (1024, 1):
+                chl_col = chl_f
+            else:
+                raise ValueError("Non-ESH left channel frame_F must be shape (1024,) or (1024, 1).")
+
+            if chr_f.shape == (1024,):
+                chr_col = chr_f.reshape(1024, 1)
+            elif chr_f.shape == (1024, 1):
+                chr_col = chr_f
+            else:
+                raise ValueError("Non-ESH right channel frame_F must be shape (1024,) or (1024, 1).")
+
+            frame_f = np.empty((1024, 2), dtype=np.float64)
+            frame_f[:, 0] = chl_col[:, 0]
+            frame_f[:, 1] = chr_col[:, 0]
+
+        frame_t_hat: FrameT = aac_i_filter_bank(frame_f, frame_type, win_type)
+
+        start = i * hop
+        y_pad[start : start + win, :] += frame_t_hat
+
+    y = aac_remove_padding(y_pad, hop=hop)
+
+    sf.write(str(filename_out), y, 48000)
+    return y

source/core/aac_snr_db.py

@@ -0,0 +1,60 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - SNR dB calculator
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   This module implements SNR calculation in dB
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from core.aac_types import StereoSignal
+import numpy as np
+
+def snr_db(x_ref: StereoSignal, x_hat: StereoSignal) -> float:
+    """
+    Compute overall SNR (dB) over all samples and channels after aligning lengths.
+
+    Parameters
+    ----------
+    x_ref : StereoSignal
+        Reference stereo stream.
+    x_hat : StereoSignal
+        Reconstructed stereo stream.
+
+    Returns
+    -------
+    float
+        SNR in dB.
+        - Returns +inf if noise power is zero.
+        - Returns -inf if signal power is zero.
+    """
+    x_ref = np.asarray(x_ref, dtype=np.float64)
+    x_hat = np.asarray(x_hat, dtype=np.float64)
+
+    if x_ref.ndim == 1:
+        x_ref = x_ref.reshape(-1, 1)
+    if x_hat.ndim == 1:
+        x_hat = x_hat.reshape(-1, 1)
+
+    n = min(x_ref.shape[0], x_hat.shape[0])
+    c = min(x_ref.shape[1], x_hat.shape[1])
+
+    x_ref = x_ref[:n, :c]
+    x_hat = x_hat[:n, :c]
+
+    err = x_ref - x_hat
+    ps = float(np.sum(x_ref * x_ref))
+    pn = float(np.sum(err * err))
+
+    if pn <= 0.0:
+        return float("inf")
+    if ps <= 0.0:
+        return float("-inf")
+
+    return float(10.0 * np.log10(ps / pn))

source/core/aac_tns.py

@@ -0,0 +1,549 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - Temporal Noise Shaping (TNS)
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   Temporal Noise Shaping (TNS) module (Level 2).
+#
+#   Public API:
+#     frame_F_out, tns_coeffs = aac_tns(frame_F_in, frame_type)
+#     frame_F_out = aac_i_tns(frame_F_in, frame_type, tns_coeffs)
+#
+#   Notes (per assignment):
+#   - TNS is applied per channel (not stereo).
+#   - For ESH, TNS is applied independently to each of the 8 short subframes.
+#   - Bark band tables are taken from TableB.2.1.9a (long) and TableB.2.1.9b (short)
+#     provided in TableB219.mat.
+#   - Predictor order is fixed to p = 4.
+#   - Coefficients are quantized with a 4-bit uniform symmetric quantizer, step = 0.1.
+#   - Forward TNS applies FIR: H_TNS(z) = 1 - a1 z^-1 - ... - ap z^-p
+#   - Inverse TNS applies the inverse IIR filter using the same quantized coefficients.
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+from scipy.io import loadmat
+
+from core.aac_configuration import PRED_ORDER, QUANT_STEP, QUANT_MAX
+from core.aac_types import *
+
+# -----------------------------------------------------------------------------
+# Private helpers
+# -----------------------------------------------------------------------------
+
+_B219_CACHE: dict[str, FloatArray] | None = None
+
+
+def _load_b219_tables() -> dict[str, FloatArray]:
+    """
+    Load TableB219.mat and cache the contents.
+
+    The project layout guarantees that a 'material' directory is discoverable
+    from the current working directory (tests and level_123 entrypoints).
+
+    Returns
+    -------
+    dict[str, FloatArray]
+        Keys:
+        - "B219a": long bands table (for K=1024 MDCT lines)
+        - "B219b": short bands table (for K=128 MDCT lines)
+    """
+    global _B219_CACHE
+    if _B219_CACHE is not None:
+        return _B219_CACHE
+
+    mat_path = Path("material") / "TableB219.mat"
+    if not mat_path.exists():
+        raise FileNotFoundError("Could not locate material/TableB219.mat in the current working directory.")
+
+    d = loadmat(str(mat_path))
+    if "B219a" not in d or "B219b" not in d:
+        raise ValueError("TableB219.mat missing required variables B219a and/or B219b.")
+
+    _B219_CACHE = {
+        "B219a": np.asarray(d["B219a"], dtype=np.float64),
+        "B219b": np.asarray(d["B219b"], dtype=np.float64),
+    }
+    return _B219_CACHE
+
+
+def _band_ranges_for_kcount(k_count: int) -> BandRanges:
+    """
+    Return Bark band index ranges [start, end] (inclusive) for the given MDCT line count.
+
+    Parameters
+    ----------
+    k_count : int
+        Number of MDCT lines:
+        - 1024 for long frames
+        - 128 for short subframes (ESH)
+
+    Returns
+    -------
+    BandRanges (list[tuple[int, int]])
+        Each tuple is (start_k, end_k) inclusive.
+    """
+    tables = _load_b219_tables()
+    if k_count == 1024:
+        tbl = tables["B219a"]
+    elif k_count == 128:
+        tbl = tables["B219b"]
+    else:
+        raise ValueError("TNS supports only k_count=1024 (long) or k_count=128 (short).")
+
+    start = tbl[:, 1].astype(int)
+    end = tbl[:, 2].astype(int)
+
+    ranges: list[tuple[int, int]] = [(int(s), int(e)) for s, e in zip(start, end)]
+
+    for s, e in ranges:
+        if s < 0 or e < s or e >= k_count:
+            raise ValueError("Invalid band table ranges for given k_count.")
+    return ranges
+
+
+# -----------------------------------------------------------------------------
+# Core DSP helpers
+# -----------------------------------------------------------------------------
+
+def _smooth_sw_inplace(sw: MdctCoeffs) -> None:
+    """
+    Smooth Sw(k) to reduce discontinuities between adjacent Bark bands.
+
+    The assignment applies two passes:
+    - Backward: Sw(k) = (Sw(k) + Sw(k+1))/2
+    - Forward:  Sw(k) = (Sw(k) + Sw(k-1))/2
+
+    Parameters
+    ----------
+    sw : MdctCoeffs
+        1-D array of length K (float64). Modified in-place.
+    """
+    k_count = int(sw.shape[0])
+
+    for k in range(k_count - 2, -1, -1):
+        sw[k] = 0.5 * (sw[k] + sw[k + 1])
+
+    for k in range(1, k_count):
+        sw[k] = 0.5 * (sw[k] + sw[k - 1])
+
+
+def _compute_sw(x: MdctCoeffs) -> MdctCoeffs:
+    """
+    Compute Sw(k) from band energies P(j) and apply boundary smoothing.
+
+    Parameters
+    ----------
+    x : MdctCoeffs
+        1-D MDCT line array, length K.
+
+    Returns
+    -------
+    MdctCoeffs
+        Sw(k), 1-D array of length K, float64.
+    """
+    x = np.asarray(x, dtype=np.float64).reshape(-1)
+    k_count = int(x.shape[0])
+
+    bands = _band_ranges_for_kcount(k_count)
+    sw = np.zeros(k_count, dtype=np.float64)
+
+    for s, e in bands:
+        seg = x[s : e + 1]
+        p_j = float(np.sum(seg * seg))
+        sw_val = float(np.sqrt(p_j))
+        sw[s : e + 1] = sw_val
+
+    _smooth_sw_inplace(sw)
+    return sw
+
+
+def _autocorr(x: MdctCoeffs, p: int) -> MdctCoeffs:
+    """
+    Autocorrelation r(m) for m=0..p.
+
+    Parameters
+    ----------
+    x : MdctCoeffs
+        1-D signal.
+    p : int
+        Maximum lag.
+
+    Returns
+    -------
+    MdctCoeffs
+        r, shape (p+1,), float64.
+    """
+    x = np.asarray(x, dtype=np.float64).reshape(-1)
+    n = int(x.shape[0])
+
+    r = np.zeros(p + 1, dtype=np.float64)
+    for m in range(p + 1):
+        r[m] = float(np.dot(x[m:], x[: n - m]))
+    return r
+
+
+def _lpc_coeffs(xw: MdctCoeffs, p: int) -> MdctCoeffs:
+    """
+    Solve Yule-Walker normal equations for LPC coefficients of order p.
+
+    Parameters
+    ----------
+    xw : MdctCoeffs
+        1-D normalized sequence Xw(k).
+    p : int
+        Predictor order.
+
+    Returns
+    -------
+    MdctCoeffs
+        LPC coefficients a[0..p-1], shape (p,), float64.
+    """
+    r = _autocorr(xw, p)
+
+    R = np.empty((p, p), dtype=np.float64)
+    for i in range(p):
+        for j in range(p):
+            R[i, j] = r[abs(i - j)]
+
+    rhs = r[1 : p + 1].reshape(p)
+
+    reg = 1e-12
+    R_reg = R + reg * np.eye(p, dtype=np.float64)
+
+    a = np.linalg.solve(R_reg, rhs)
+    return a
+
+
+def _quantize_coeffs(a: MdctCoeffs) -> MdctCoeffs:
+    """
+    Quantize LPC coefficients with uniform symmetric quantizer and clamp.
+
+    Parameters
+    ----------
+    a : MdctCoeffs
+        LPC coefficient array, shape (p,).
+
+    Returns
+    -------
+    MdctCoeffs
+        Quantized coefficients, shape (p,), float64.
+    """
+    a = np.asarray(a, dtype=np.float64).reshape(-1)
+    q = np.round(a / QUANT_STEP) * QUANT_STEP
+    q = np.clip(q, -QUANT_MAX, QUANT_MAX)
+    return q.astype(np.float64, copy=False)
+
+
+def _is_inverse_stable(a_q: MdctCoeffs) -> bool:
+    """
+    Check stability of the inverse TNS filter H_TNS^{-1}.
+
+    Forward filter:
+        H_TNS(z) = 1 - a1 z^-1 - ... - ap z^-p
+
+    Inverse filter poles are roots of:
+        A(z) = 1 - a1 z^-1 - ... - ap z^-p
+    Multiply by z^p:
+        z^p - a1 z^{p-1} - ... - ap = 0
+
+    Stability condition:
+        all roots satisfy |z| < 1.
+
+    Parameters
+    ----------
+    a_q : MdctCoeffs
+        Quantized predictor coefficients, shape (p,).
+
+    Returns
+    -------
+    bool
+        True if stable, else False.
+    """
+    a_q = np.asarray(a_q, dtype=np.float64).reshape(-1)
+    p = int(a_q.shape[0])
+
+    # Polynomial in z: z^p - a1 z^{p-1} - ... - ap
+    poly = np.empty(p + 1, dtype=np.float64)
+    poly[0] = 1.0
+    poly[1:] = -a_q
+
+    roots = np.roots(poly)
+
+    # Strictly inside unit circle for stability. Add tiny margin for numeric safety.
+    margin = 1e-12
+    return bool(np.all(np.abs(roots) < (1.0 - margin)))
+
+
+def _stabilize_quantized_coeffs(a_q: MdctCoeffs) -> MdctCoeffs:
+    """
+    Make quantized predictor coefficients stable for inverse filtering.
+
+    Policy:
+    - If already stable: return as-is.
+    - Else: iteratively shrink coefficients by gamma and re-quantize to the 0.1 grid.
+    - If still unstable after attempts: fall back to all-zero coefficients (disable TNS).
+
+    Parameters
+    ----------
+    a_q : MdctCoeffs
+        Quantized predictor coefficients, shape (p,).
+
+    Returns
+    -------
+    MdctCoeffs
+        Stable quantized coefficients, shape (p,).
+    """
+    a_q = np.asarray(a_q, dtype=np.float64).reshape(-1)
+
+    if _is_inverse_stable(a_q):
+        return a_q
+
+    # Try a few shrinking factors. Re-quantize after shrinking to keep coefficients on-grid.
+    gammas = (0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1)
+
+    for g in gammas:
+        cand = _quantize_coeffs(g * a_q)
+        if _is_inverse_stable(cand):
+            return cand
+
+    # Last resort: disable TNS for this vector
+    return np.zeros_like(a_q, dtype=np.float64)
+
+
+def _apply_tns_fir(x: MdctCoeffs, a_q: MdctCoeffs) -> MdctCoeffs:
+    """
+    Apply forward TNS FIR filter:
+        y[k] = x[k] - sum_{l=1..p} a_l * x[k-l]
+
+    Parameters
+    ----------
+    x : MdctCoeffs
+        1-D MDCT lines, length K.
+    a_q : MdctCoeffs
+        Quantized LPC coefficients, shape (p,).
+
+    Returns
+    -------
+    MdctCoeffs
+        Filtered MDCT lines y, length K.
+    """
+    x = np.asarray(x, dtype=np.float64).reshape(-1)
+    a_q = np.asarray(a_q, dtype=np.float64).reshape(-1)
+    p = int(a_q.shape[0])
+    k_count = int(x.shape[0])
+
+    y = np.zeros(k_count, dtype=np.float64)
+    for k in range(k_count):
+        acc = x[k]
+        for l in range(1, p + 1):
+            if k - l >= 0:
+                acc -= a_q[l - 1] * x[k - l]
+        y[k] = acc
+    return y
+
+
+def _apply_itns_iir(y: MdctCoeffs, a_q: MdctCoeffs) -> MdctCoeffs:
+    """
+    Apply inverse TNS IIR filter:
+        x_hat[k] = y[k] + sum_{l=1..p} a_l * x_hat[k-l]
+
+    Parameters
+    ----------
+    y : MdctCoeffs
+        1-D MDCT lines after TNS, length K.
+    a_q : MdctCoeffs
+        Quantized LPC coefficients, shape (p,).
+
+    Returns
+    -------
+    MdctCoeffs
+        Reconstructed MDCT lines x_hat, length K.
+    """
+    y = np.asarray(y, dtype=np.float64).reshape(-1)
+    a_q = np.asarray(a_q, dtype=np.float64).reshape(-1)
+    p = int(a_q.shape[0])
+    k_count = int(y.shape[0])
+
+    x_hat = np.zeros(k_count, dtype=np.float64)
+    for k in range(k_count):
+        acc = y[k]
+        for l in range(1, p + 1):
+            if k - l >= 0:
+                acc += a_q[l - 1] * x_hat[k - l]
+        x_hat[k] = acc
+    return x_hat
+
+
+def _tns_one_vector(x: MdctCoeffs) -> tuple[MdctCoeffs, MdctCoeffs]:
+    """
+    TNS for a single MDCT vector (one long frame or one short subframe).
+
+    Steps:
+    1) Compute Sw(k) from Bark band energies and smooth it.
+    2) Normalize: Xw(k) = X(k) / Sw(k) (safe when Sw=0).
+    3) Compute LPC coefficients (order p=PRED_ORDER) on Xw.
+    4) Quantize coefficients (4-bit symmetric, step QUANT_STEP).
+    5) Apply FIR filter on original X(k) using quantized coefficients.
+
+    Parameters
+    ----------
+    x : MdctCoeffs
+        1-D MDCT vector.
+
+    Returns
+    -------
+    y : MdctCoeffs
+        TNS-processed MDCT vector (same length).
+    a_q : MdctCoeffs
+        Quantized LPC coefficients, shape (PRED_ORDER,).
+    """
+    x = np.asarray(x, dtype=np.float64).reshape(-1)
+    sw = _compute_sw(x)
+
+    eps = 1e-12
+    xw = np.where(sw > eps, x / sw, 0.0)
+
+    a = _lpc_coeffs(xw, PRED_ORDER)
+    a_q = _quantize_coeffs(a)
+
+    # Ensure inverse stability (assignment requirement)
+    a_q = _stabilize_quantized_coeffs(a_q)
+
+    y = _apply_tns_fir(x, a_q)
+    return y, a_q
+
+
+
+# -----------------------------------------------------------------------------
+# Public Functions (Level 2)
+# -----------------------------------------------------------------------------
+
+def aac_tns(frame_F_in: FrameChannelF, frame_type: FrameType) -> Tuple[FrameChannelF, TnsCoeffs]:
+    """
+    Temporal Noise Shaping (TNS) for ONE channel.
+
+    Parameters
+    ----------
+    frame_F_in : FrameChannelF
+        Per-channel MDCT coefficients.
+        Expected (typical) shapes:
+        - If frame_type == "ESH": (128, 8)
+        - Else: (1024, 1) or (1024,)
+
+    frame_type : FrameType
+        Frame type code ("OLS", "LSS", "ESH", "LPS").
+
+    Returns
+    -------
+    frame_F_out : FrameChannelF
+        Per-channel MDCT coefficients after applying TNS.
+        Same shape convention as input.
+
+    tns_coeffs : TnsCoeffs
+        Quantized TNS predictor coefficients.
+        Expected shapes:
+        - If frame_type == "ESH": (PRED_ORDER, 8)
+        - Else: (PRED_ORDER, 1)
+    """
+    x = np.asarray(frame_F_in, dtype=np.float64)
+
+    if frame_type == "ESH":
+        if x.shape != (128, 8):
+            raise ValueError("For ESH, frame_F_in must have shape (128, 8).")
+
+        y = np.empty_like(x, dtype=np.float64)
+        a_out = np.empty((PRED_ORDER, 8), dtype=np.float64)
+
+        for j in range(8):
+            y[:, j], a_out[:, j] = _tns_one_vector(x[:, j])
+
+        return y, a_out
+
+    if x.shape == (1024,):
+        x_vec = x
+        out_shape = (1024,)
+    elif x.shape == (1024, 1):
+        x_vec = x[:, 0]
+        out_shape = (1024, 1)
+    else:
+        raise ValueError('For non-ESH, frame_F_in must have shape (1024,) or (1024, 1).')
+
+    y_vec, a_q = _tns_one_vector(x_vec)
+
+    if out_shape == (1024,):
+        y_out = y_vec
+    else:
+        y_out = y_vec.reshape(1024, 1)
+
+    a_out = a_q.reshape(PRED_ORDER, 1)
+    return y_out, a_out
+
+
+def aac_i_tns(frame_F_in: FrameChannelF, frame_type: FrameType, tns_coeffs: TnsCoeffs) -> FrameChannelF:
+    """
+    Inverse Temporal Noise Shaping (iTNS) for ONE channel.
+
+    Parameters
+    ----------
+    frame_F_in : FrameChannelF
+        Per-channel MDCT coefficients after TNS.
+        Expected (typical) shapes:
+        - If frame_type == "ESH": (128, 8)
+        - Else: (1024, 1) or (1024,)
+
+    frame_type : FrameType
+        Frame type code ("OLS", "LSS", "ESH", "LPS").
+
+    tns_coeffs : TnsCoeffs
+        Quantized TNS predictor coefficients.
+        Expected shapes:
+        - If frame_type == "ESH": (PRED_ORDER, 8)
+        - Else: (PRED_ORDER, 1)
+
+    Returns
+    -------
+    FrameChannelF
+        Per-channel MDCT coefficients after inverse TNS.
+        Same shape convention as input frame_F_in.
+    """
+    x = np.asarray(frame_F_in, dtype=np.float64)
+    a = np.asarray(tns_coeffs, dtype=np.float64)
+
+    if frame_type == "ESH":
+        if x.shape != (128, 8):
+            raise ValueError("For ESH, frame_F_in must have shape (128, 8).")
+        if a.shape != (PRED_ORDER, 8):
+            raise ValueError("For ESH, tns_coeffs must have shape (PRED_ORDER, 8).")
+
+        y = np.empty_like(x, dtype=np.float64)
+        for j in range(8):
+            y[:, j] = _apply_itns_iir(x[:, j], a[:, j])
+        return y
+
+    if a.shape != (PRED_ORDER, 1):
+        raise ValueError("For non-ESH, tns_coeffs must have shape (PRED_ORDER, 1).")
+
+    if x.shape == (1024,):
+        x_vec = x
+        out_shape = (1024,)
+    elif x.shape == (1024, 1):
+        x_vec = x[:, 0]
+        out_shape = (1024, 1)
+    else:
+        raise ValueError('For non-ESH, frame_F_in must have shape (1024,) or (1024, 1).')
+
+    y_vec = _apply_itns_iir(x_vec, a[:, 0])
+
+    if out_shape == (1024,):
+        return y_vec
+    return y_vec.reshape(1024, 1)

source/core/aac_types.py

@@ -10,7 +10,6 @@
 #
 # Description:
 #   This module implements Public Type aliases
-#
 # ------------------------------------------------------------
 from __future__ import annotations
 
@@ -39,7 +38,7 @@ Window type codes (AAC):
 """
 
 ChannelKey: TypeAlias = Literal["chl", "chr"]
-"""Channel dictionary keys used in Level 1 payloads."""
+"""Channel dictionary keys used in Level payloads."""
 
 
 # -----------------------------------------------------------------------------
@@ -105,6 +104,40 @@ Examples:
 dtype: float64
 """
 
+MdctFrameChannel: TypeAlias = FloatArray
+"""
+Per-channel MDCT container used in Level-1/2 sequences.
+
+Typical shapes:
+- If frame_type in {"OLS","LSS","LPS"}: (1024, 1) or (1024,)
+- If frame_type == "ESH": (128, 8)  (8 short subframes for one channel)
+
+dtype: float64
+
+Notes
+-----
+Some parts of the assignment store long-frame coefficients as a column vector
+(1024, 1) to match MATLAB conventions. Internally you may also use (1024,)
+when convenient, but the semantic meaning is identical.
+"""
+
+TnsCoeffs: TypeAlias = FloatArray
+"""
+Quantized TNS predictor coefficients (one channel).
+
+Typical shapes (Level 2):
+- If frame_type == "ESH": (4, 8)   (order p=4 for each of the 8 short subframes)
+- Else: (4, 1)            (order p=4 for the long frame)
+
+dtype: float64
+
+Notes
+-----
+The assignment uses a 4-bit uniform symmetric quantizer with step size 0.1.
+We store the quantized coefficient values as float64 (typically multiples of 0.1)
+to keep the pipeline simple and readable.
+"""
+
 
 FrameT: TypeAlias = FloatArray
 """
@@ -142,17 +175,23 @@ Rationale for ESH (128, 16):
 dtype: float64
 """
 
-FrameChannelF: TypeAlias = FloatArray
+FrameChannelF: TypeAlias = MdctFrameChannel
 """
-Frequency-domain single-channel frame (MDCT coefficients).
+Frequency-domain single-channel MDCT coefficients.
 
-Typical shapes (Level 1):
+Typical shapes (Level 1/2):
-- If frame_type in {"OLS","LSS","LPS"}: (1024,)
+- If frame_type in {"OLS","LSS","LPS"}: (1024, 1) or (1024,)
-- If frame_type == "ESH": (128, 8)  (8 short subframes for one channel)
+- If frame_type == "ESH": (128, 8)
 
 dtype: float64
 """
 
+BandRanges: TypeAlias = list[tuple[int, int]]
+"""
+Bark-band index ranges [start, end] (inclusive) for MDCT lines.
+
+Used by TNS to map MDCT indices k to Bark bands.
+"""
 
 # -----------------------------------------------------------------------------
 # Level 1 AAC sequence payload types
@@ -168,7 +207,7 @@ class AACChannelFrameF(TypedDict):
         The MDCT coefficients for ONE channel.
         Typical shapes:
         - ESH: (128, 8)   (8 short subframes)
-        - else: (1024, )
+        - else: (1024, 1) or (1024,)
     """
     frame_F: FrameChannelF
 
@@ -191,3 +230,53 @@ List of length K (K = number of frames).
 Each element is a dict with keys:
 - "frame_type", "win_type", "chl", "chr"
 """
+
+
+# -----------------------------------------------------------------------------
+# Level 2 AAC sequence payload types (TNS)
+# -----------------------------------------------------------------------------
+
+class AACChannelFrameF2(TypedDict):
+    """
+    Per-channel payload for aac_seq_2[i]["chl"] or ["chr"] (Level 2).
+
+    Keys
+    ----
+    frame_F:
+        The TNS-processed MDCT coefficients for ONE channel.
+        Typical shapes:
+        - ESH: (128, 8)
+        - else: (1024, 1) or (1024,)
+    tns_coeffs:
+        Quantized TNS predictor coefficients for ONE channel.
+        Typical shapes:
+        - ESH: (PRED_ORDER, 8)
+        - else: (PRED_ORDER, 1)
+    """
+    frame_F: FrameChannelF
+    tns_coeffs: TnsCoeffs
+
+
+class AACSeq2Frame(TypedDict):
+    """
+    One frame dictionary element of aac_seq_2 (Level 2).
+    """
+    frame_type: FrameType
+    win_type: WinType
+    chl: AACChannelFrameF2
+    chr: AACChannelFrameF2
+
+
+AACSeq2: TypeAlias = List[AACSeq2Frame]
+"""
+AAC sequence for Level 2:
+List of length K (K = number of frames).
+
+Each element is a dict with keys:
+- "frame_type", "win_type", "chl", "chr"
+
+Level 2 adds:
+- per-channel "tns_coeffs"
+and stores:
+- per-channel "frame_F" after applying TNS.
+"""

source/core/tests/test_aac_coder_decoder.py

@@ -19,59 +19,15 @@ import numpy as np
 import pytest
 import soundfile as sf
 
-from core.aac_coder import aac_coder_1
+from core.aac_coder import aac_coder_1, aac_coder_2, aac_read_wav_stereo_48k
-from core.aac_decoder import aac_decoder_1
+from core.aac_decoder import aac_decoder_1, aac_decoder_2, aac_remove_padding
 from core.aac_types import *
+from core.aac_snr_db import snr_db
 
 
 # Helper "fixtures" for aac_coder_1 / i_aac_coder_1
 # -----------------------------------------------------------------------------
 
-def _snr_db(x_ref: StereoSignal, x_hat: StereoSignal) -> float:
-    """
-    Compute overall SNR (dB) over all samples and channels after aligning lengths.
-
-    Parameters
-    ----------
-    x_ref : StereoSignal
-        Reference signal, shape (N, 2) typical.
-    x_hat : StereoSignal
-        Reconstructed signal, shape (M, 2) typical.
-
-    Returns
-    -------
-    float
-        SNR in dB.
-        - Returns +inf if noise power is zero.
-        - Returns -inf if signal power is zero.
-    """
-    x_ref = np.asarray(x_ref, dtype=np.float64)
-    x_hat = np.asarray(x_hat, dtype=np.float64)
-
-    # Be conservative: align lengths and common channels.
-    if x_ref.ndim == 1:
-        x_ref = x_ref.reshape(-1, 1)
-    if x_hat.ndim == 1:
-        x_hat = x_hat.reshape(-1, 1)
-
-    n = min(x_ref.shape[0], x_hat.shape[0])
-    c = min(x_ref.shape[1], x_hat.shape[1])
-
-    x_ref = x_ref[:n, :c]
-    x_hat = x_hat[:n, :c]
-
-    err = x_ref - x_hat
-    ps = float(np.sum(x_ref * x_ref))
-    pn = float(np.sum(err * err))
-
-    if pn <= 0.0:
-        return float("inf")
-    if ps <= 0.0:
-        return float("-inf")
-
-    return float(10.0 * np.log10(ps / pn))
-
-
 @pytest.fixture()
 def tmp_stereo_wav(tmp_path: Path) -> Path:
     """
@@ -89,6 +45,56 @@ def tmp_stereo_wav(tmp_path: Path) -> Path:
     return wav_path
 
 
+# -----------------------------------------------------------------------------
+# Helper-function tests
+# -----------------------------------------------------------------------------
+
+def test_aac_read_wav_stereo_48k_roundtrip(tmp_stereo_wav: Path) -> None:
+    """
+    Contract test for aac_read_wav_stereo_48k():
+    - Reads stereo WAV
+    - Returns float64 array with shape (N,2)
+    - Returns fs = 48000
+    """
+    x, fs = aac_read_wav_stereo_48k(tmp_stereo_wav)
+
+    assert int(fs) == 48000
+    assert isinstance(x, np.ndarray)
+    assert x.dtype == np.float64
+    assert x.ndim == 2
+    assert x.shape[1] == 2
+    assert x.shape[0] > 0
+
+
+def test_aac_remove_padding_removes_hop_from_both_ends() -> None:
+    """
+    Contract test for aac_remove_padding():
+    - Removes 'hop' samples from start and end.
+    """
+    hop = 1024
+    n = 10000
+
+    y_pad: StereoSignal = np.zeros((n, 2), dtype=np.float64)
+    y: StereoSignal = aac_remove_padding(y_pad, hop=hop)
+
+    assert y.shape == (n - 2 * hop, 2)
+    assert y.dtype == np.float64
+
+
+def test_aac_remove_padding_errors_on_too_short_input() -> None:
+    """
+    aac_remove_padding must raise if y_pad is shorter than 2*hop.
+    """
+    hop = 1024
+    y_pad: StereoSignal = np.zeros((2 * hop - 1, 2), dtype=np.float64)
+
+    with pytest.raises(ValueError):
+        _ = aac_remove_padding(y_pad, hop=hop)
+
+
+# -----------------------------------------------------------------------------
+# Level 1 tests
+# -----------------------------------------------------------------------------
 def test_aac_coder_seq_schema_and_shapes(tmp_stereo_wav: Path) -> None:
     """
     Module-level contract test:
@@ -152,5 +158,68 @@ def test_end_to_end_aac_coder_decoder_high_snr(tmp_stereo_wav: Path, tmp_path: P
     assert int(fs_hat) == 48000
 
     # SNR against returned array (file should match closely, but we do not require it here).
-    snr = _snr_db(x_ref, x_hat)
+    snr = snr_db(x_ref, x_hat)
     assert snr > 80.0
+
+# -----------------------------------------------------------------------------
+# Level 2 tests (new)
+# -----------------------------------------------------------------------------
+
+def test_aac_coder_2_seq_schema_and_shapes(tmp_stereo_wav: Path) -> None:
+    """
+    Module-level contract test (Level 2):
+    Ensure aac_seq_2 follows the expected schema and per-frame shapes, including tns_coeffs.
+    """
+    aac_seq: AACSeq2 = aac_coder_2(tmp_stereo_wav)
+
+    assert isinstance(aac_seq, list)
+    assert len(aac_seq) > 0
+
+    for fr in aac_seq:
+        assert "frame_type" in fr
+        assert "win_type" in fr
+        assert "chl" in fr
+        assert "chr" in fr
+
+        frame_type: FrameType = fr["frame_type"]
+        assert frame_type in ("OLS", "LSS", "ESH", "LPS")
+
+        for ch_key in ("chl", "chr"):
+            ch = fr[ch_key]
+            assert "frame_F" in ch
+            assert "tns_coeffs" in ch
+
+            frame_f = np.asarray(ch["frame_F"], dtype=np.float64)
+            coeffs = np.asarray(ch["tns_coeffs"], dtype=np.float64)
+
+            if frame_type == "ESH":
+                assert frame_f.shape == (128, 8)
+                assert coeffs.shape[0] == 4
+                assert coeffs.shape[1] == 8
+            else:
+                assert frame_f.shape == (1024, 1)
+                assert coeffs.shape == (4, 1)
+
+
+def test_end_to_end_level_2_high_snr(tmp_stereo_wav: Path, tmp_path: Path) -> None:
+    """
+    End-to-end test (Level 2):
+    Encode + decode and check SNR remains very high.
+
+    Level 2 is still floating-point (TNS is reversible), so reconstruction
+    should remain numerical-noise only.
+    """
+    x_ref, fs = sf.read(str(tmp_stereo_wav), always_2d=True)
+    x_ref = np.asarray(x_ref, dtype=np.float64)
+    assert int(fs) == 48000
+
+    out_wav = tmp_path / "out_l2.wav"
+    aac_seq = aac_coder_2(tmp_stereo_wav)
+    x_hat: StereoSignal = aac_decoder_2(aac_seq, out_wav)
+
+    assert out_wav.exists()
+    _, fs_hat = sf.read(str(out_wav), always_2d=True)
+    assert int(fs_hat) == 48000
+
+    snr = snr_db(x_ref, x_hat)
+    assert snr > 75.0

source/core/tests/test_filterbank.py

@@ -17,6 +17,7 @@ from typing import Sequence
 import pytest
 
 from core.aac_filterbank import aac_filter_bank, aac_i_filter_bank
+from core.aac_snr_db import snr_db
 from core.aac_types import *
 
 # Helper fixtures for filterbank
@@ -56,20 +57,6 @@ def _ola_reconstruct(x: StereoSignal, frame_types: Sequence[FrameType], win_type
     return y
 
 
-def _snr_db(x: StereoSignal, y: StereoSignal) -> float:
-    """
-    Compute SNR in dB over all samples/channels.
-    """
-    err = x - y
-    ps = float(np.sum(x * x))
-    pn = float(np.sum(err * err))
-    if pn <= 0.0:
-        return float("inf")
-    if ps <= 0.0:
-        return float("-inf")
-    return 10.0 * float(np.log10(ps / pn))
-
-
 # -----------------------------------------------------------------------------
 # Forward filterbank tests
 # -----------------------------------------------------------------------------
@@ -223,7 +210,7 @@ def test_ola_reconstruction_ols_high_snr(win_type: WinType) -> None:
 
     a = 1024
     b = N - 1024
-    snr = _snr_db(x[a:b, :], y[a:b, :])
+    snr = snr_db(x[a:b, :], y[a:b, :])
     assert snr > 50.0
 
 
@@ -244,7 +231,7 @@ def test_ola_reconstruction_esh_high_snr(win_type: WinType) -> None:
 
     a = 1024
     b = N - 1024
-    snr = _snr_db(x[a:b, :], y[a:b, :])
+    snr = snr_db(x[a:b, :], y[a:b, :])
     assert snr > 45.0
 
 
@@ -265,5 +252,5 @@ def test_ola_reconstruction_transition_sequence(win_type: WinType) -> None:
 
     a = 1024
     b = N - 1024
-    snr = _snr_db(x[a:b, :], y[a:b, :])
+    snr = snr_db(x[a:b, :], y[a:b, :])
     assert snr > 40.0

source/core/tests/test_snr_db.py

@@ -0,0 +1,98 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - SNR dB Tests
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   Basic tests for SNR calculation utility.
+# ------------------------------------------------------------
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from core.aac_snr_db import snr_db
+from core.aac_types import StereoSignal
+
+
+def test_snr_perfect_reconstruction_returns_inf() -> None:
+    """
+    If x_hat == x_ref exactly, noise power is zero and SNR must be +inf.
+    """
+    rng = np.random.default_rng(0)
+    x: StereoSignal = rng.normal(size=(1024, 2)).astype(np.float64)
+
+    snr = snr_db(x, x)
+    assert snr == float("inf")
+
+
+def test_snr_zero_reference_returns_minus_inf() -> None:
+    """
+    If reference signal is identically zero, signal power is zero
+    and SNR must be -inf (unless noise is also zero, which is degenerate).
+    """
+    x_ref: StereoSignal = np.zeros((1024, 2), dtype=np.float64)
+    x_hat: StereoSignal = np.ones((1024, 2), dtype=np.float64)
+
+    snr = snr_db(x_ref, x_hat)
+    assert snr == float("-inf")
+
+
+def test_snr_known_noise_level_matches_expected_value() -> None:
+    """
+    Deterministic test with known signal and noise power.
+
+    Let:
+      x_ref = ones
+      x_hat = ones + noise
+
+    With noise variance sigma^2, expected SNR:
+      10 * log10(Ps / Pn)
+    """
+    n = 1000
+    sigma = 0.1
+
+    x_ref: StereoSignal = np.ones((n, 2), dtype=np.float64)
+    noise = sigma * np.ones((n, 2), dtype=np.float64)
+    x_hat: StereoSignal = x_ref + noise
+
+    ps = float(np.sum(x_ref * x_ref))
+    pn = float(np.sum(noise * noise))
+    expected = 10.0 * np.log10(ps / pn)
+
+    snr = snr_db(x_ref, x_hat)
+    assert np.isclose(snr, expected, rtol=1e-12, atol=1e-12)
+
+
+def test_snr_aligns_different_lengths_and_channels() -> None:
+    """
+    The function must:
+    - align to minimum length
+    - align to minimum channel count
+    without crashing.
+    """
+    rng = np.random.default_rng(1)
+
+    x_ref: StereoSignal = rng.normal(size=(1000, 2)).astype(np.float64)
+    x_hat: StereoSignal = rng.normal(size=(800, 1)).astype(np.float64)
+
+    snr = snr_db(x_ref, x_hat)
+    assert np.isfinite(snr)
+
+
+def test_snr_accepts_1d_inputs() -> None:
+    """
+    1-D inputs must be accepted and treated as single-channel signals.
+    """
+    rng = np.random.default_rng(2)
+
+    x_ref = rng.normal(size=1024).astype(np.float64)
+    x_hat = x_ref + 0.01 * rng.normal(size=1024).astype(np.float64)
+
+    snr = snr_db(x_ref, x_hat)
+    assert np.isfinite(snr)

source/core/tests/test_tns.py

@@ -0,0 +1,196 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - TNS Tests
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   Tests for Temporal Noise Shaping (TNS) module (Level 2).
+#
+#   Validates:
+#   - I/O shapes for long and ESH modes
+#   - Quantization grid and clamping of predictor coefficients
+#   - Inverse-filter stability (all poles inside unit circle)
+#   - Functional correctness: iTNS(TNS(X)) ≈ X
+# ------------------------------------------------------------
+
+from __future__ import annotations
+
+import pytest
+
+from core.aac_configuration import PRED_ORDER, QUANT_MAX, QUANT_STEP
+from core.aac_tns import aac_tns, aac_i_tns
+from core.aac_types import *
+
+
+# -----------------------------------------------------------------------------
+# Helper utilities
+# -----------------------------------------------------------------------------
+
+def _is_inverse_stable_from_coeffs(a_q: MdctCoeffs) -> bool:
+    """
+    Check stability of the inverse TNS filter H_TNS^{-1}.
+
+    Poles are roots of:
+        z^p - a1 z^{p-1} - ... - ap = 0
+
+    Stability condition:
+        |root| < 1 for all roots.
+    """
+    a_q = np.asarray(a_q, dtype=np.float64).reshape(-1)
+    p = int(a_q.shape[0])
+
+    poly = np.empty(p + 1, dtype=np.float64)
+    poly[0] = 1.0
+    poly[1:] = -a_q
+
+    roots = np.roots(poly)
+    margin = 1e-12
+    return bool(np.all(np.abs(roots) < (1.0 - margin)))
+
+
+def _assert_quantized_and_clamped(a_q: MdctCoeffs) -> None:
+    """
+    Assert that coefficients:
+    - lie on the QUANT_STEP grid
+    - are clamped to [-QUANT_MAX, +QUANT_MAX]
+    """
+    a_q = np.asarray(a_q, dtype=np.float64)
+
+    assert np.max(np.abs(a_q)) <= (QUANT_MAX + 1e-12)
+
+    grid = a_q / float(QUANT_STEP)
+    assert np.max(np.abs(grid - np.round(grid))) < 1e-12
+
+
+# -----------------------------------------------------------------------------
+# Shape / contract tests
+# -----------------------------------------------------------------------------
+
+@pytest.mark.parametrize("frame_type", ["OLS", "LSS", "LPS"])
+def test_tns_shapes_long_sequences(frame_type: FrameType) -> None:
+    """
+    Contract test (long frames):
+    - Input shape: (1024, 1)
+    - Output shape preserved
+    - Predictor coeffs shape: (PRED_ORDER, 1)
+    """
+    rng = np.random.default_rng(0)
+    frame_F_in: FrameChannelF = rng.normal(size=(1024, 1)).astype(np.float64)
+
+    frame_F_out, tns_coeffs = aac_tns(frame_F_in, frame_type)
+
+    assert frame_F_out.shape == frame_F_in.shape
+    assert tns_coeffs.shape == (PRED_ORDER, 1)
+
+
+def test_tns_shapes_esh() -> None:
+    """
+    Contract test (ESH):
+    - Input shape: (128, 8)
+    - Output shape preserved
+    - Predictor coeffs shape: (PRED_ORDER, 8)
+    """
+    rng = np.random.default_rng(1)
+    frame_F_in: FrameChannelF = rng.normal(size=(128, 8)).astype(np.float64)
+
+    frame_F_out, tns_coeffs = aac_tns(frame_F_in, "ESH")
+
+    assert frame_F_out.shape == (128, 8)
+    assert tns_coeffs.shape == (PRED_ORDER, 8)
+
+
+# -----------------------------------------------------------------------------
+# Coefficient properties
+# -----------------------------------------------------------------------------
+
+@pytest.mark.parametrize("frame_type", ["OLS", "LSS", "LPS"])
+def test_tns_coeffs_quantized_clamped_and_stable_long(frame_type: FrameType) -> None:
+    """
+    Long-frame predictor coefficients must be:
+    - quantized on QUANT_STEP grid
+    - clamped to [-QUANT_MAX, +QUANT_MAX]
+    - stable for inverse filtering
+    """
+    rng = np.random.default_rng(2)
+    frame_F_in: FrameChannelF = rng.normal(size=(1024, 1)).astype(np.float64)
+
+    _, tns_coeffs = aac_tns(frame_F_in, frame_type)
+
+    a_q: MdctCoeffs = tns_coeffs[:, 0]
+    _assert_quantized_and_clamped(a_q)
+    assert _is_inverse_stable_from_coeffs(a_q)
+
+
+def test_tns_coeffs_quantized_clamped_and_stable_esh() -> None:
+    """
+    ESH predictor coefficients must satisfy quantization and stability
+    independently for each of the 8 short subframes.
+    """
+    rng = np.random.default_rng(3)
+    frame_F_in: FrameChannelF = rng.normal(size=(128, 8)).astype(np.float64)
+
+    _, tns_coeffs = aac_tns(frame_F_in, "ESH")
+
+    for j in range(8):
+        a_q: MdctCoeffs = tns_coeffs[:, j]
+        _assert_quantized_and_clamped(a_q)
+        assert _is_inverse_stable_from_coeffs(a_q)
+
+
+# -----------------------------------------------------------------------------
+# Functional correctness (round-trip)
+# -----------------------------------------------------------------------------
+
+@pytest.mark.parametrize("frame_type", ["OLS", "LSS", "LPS"])
+def test_tns_roundtrip_long_is_close(frame_type: FrameType) -> None:
+    """
+    Functional test:
+        iTNS(TNS(X)) ≈ X   for long frames.
+    """
+    rng = np.random.default_rng(4)
+    frame_F_in: FrameChannelF = rng.normal(size=(1024, 1)).astype(np.float64)
+
+    frame_F_tns, tns_coeffs = aac_tns(frame_F_in, frame_type)
+    frame_F_hat = aac_i_tns(frame_F_tns, frame_type, tns_coeffs)
+
+    np.testing.assert_allclose(frame_F_hat, frame_F_in, rtol=1e-9, atol=1e-9)
+
+
+def test_tns_roundtrip_esh_is_close() -> None:
+    """
+    Functional test:
+        iTNS(TNS(X)) ≈ X   for ESH frames (8 independent subframes).
+    """
+    rng = np.random.default_rng(5)
+    frame_F_in: FrameChannelF = rng.normal(size=(128, 8)).astype(np.float64)
+
+    frame_F_tns, tns_coeffs = aac_tns(frame_F_in, "ESH")
+    frame_F_hat = aac_i_tns(frame_F_tns, "ESH", tns_coeffs)
+
+    np.testing.assert_allclose(frame_F_hat, frame_F_in, rtol=1e-9, atol=1e-9)
+
+
+# -----------------------------------------------------------------------------
+# Sanity
+# -----------------------------------------------------------------------------
+
+def test_tns_outputs_are_finite() -> None:
+    """
+    Sanity test: no NaN or inf in outputs.
+    """
+    rng = np.random.default_rng(6)
+
+    frame_F_long: FrameChannelF = rng.normal(size=(1024, 1)).astype(np.float64)
+    out_long, coeffs_long = aac_tns(frame_F_long, "OLS")
+    assert np.isfinite(out_long).all()
+    assert np.isfinite(coeffs_long).all()
+
+    frame_F_esh: FrameChannelF = rng.normal(size=(128, 8)).astype(np.float64)
+    out_esh, coeffs_esh = aac_tns(frame_F_esh, "ESH")
+    assert np.isfinite(out_esh).all()
+    assert np.isfinite(coeffs_esh).all()

source/level_1/core/aac_coder.py

@@ -30,6 +30,7 @@ import soundfile as sf
 from core.aac_configuration import WIN_TYPE
 from core.aac_filterbank import aac_filter_bank
 from core.aac_ssc import aac_SSC
+from core.aac_tns import aac_tns
 from core.aac_types import *
 
 
@@ -144,8 +145,8 @@ def aac_coder_1(filename_in: Union[str, Path]) -> AACSeq1:
     AACSeq1
         List of encoded frames (Level 1 schema).
     """
-    x, fs = aac_read_wav_stereo_48k(filename_in)
+    x, _ = aac_read_wav_stereo_48k(filename_in)
-    _ = fs  # kept for clarity; The assignment assumes 48 kHz
+    # The assignment assumes 48 kHz
 
     hop = 1024
     win = 2048
@@ -196,3 +197,88 @@ def aac_coder_1(filename_in: Union[str, Path]) -> AACSeq1:
         prev_frame_type = frame_type
 
     return aac_seq
+
+
+def aac_coder_2(filename_in: Union[str, Path]) -> AACSeq2:
+    """
+    Level-2 AAC encoder (Level 1 + TNS).
+
+    Parameters
+    ----------
+    filename_in : Union[str, Path]
+        Input WAV filename (stereo, 48 kHz).
+
+    Returns
+    -------
+    AACSeq2
+        Encoded AAC sequence (Level 2 payload schema).
+        For each frame i:
+        - "frame_type": FrameType
+        - "win_type": WinType
+        - "chl"/"chr":
+            - "frame_F": FrameChannelF (after TNS)
+            - "tns_coeffs": TnsCoeffs
+    """
+    filename_in = Path(filename_in)
+
+    x, _ = aac_read_wav_stereo_48k(filename_in)
+    # The assignment assumes 48 kHz
+
+    hop = 1024
+    win = 2048
+
+    pad_pre = np.zeros((hop, 2), dtype=np.float64)
+    pad_post = np.zeros((hop, 2), dtype=np.float64)
+    x_pad = np.vstack([pad_pre, x, pad_post])
+
+    K = int((x_pad.shape[0] - win) // hop + 1)
+    if K <= 0:
+        raise ValueError("Input too short for framing.")
+
+    aac_seq: AACSeq2 = []
+    prev_frame_type: FrameType = "OLS"
+
+    for i in range(K):
+        start = i * hop
+
+        frame_t: FrameT = x_pad[start : start + win, :]
+        if frame_t.shape != (win, 2):
+            raise ValueError("Internal framing error: frame_t has wrong shape.")
+
+        next_t = x_pad[start + hop : start + hop + win, :]
+        if next_t.shape[0] < win:
+            tail = np.zeros((win - next_t.shape[0], 2), dtype=np.float64)
+            next_t = np.vstack([next_t, tail])
+
+        frame_type = aac_SSC(frame_t, next_t, prev_frame_type)
+
+        # Level 1 analysis (packed stereo container)
+        frame_f_stereo = aac_filter_bank(frame_t, frame_type, WIN_TYPE)
+
+        # Unpack to per-channel (as you already do in Level 1)
+        if frame_type == "ESH":
+            chl_f = np.empty((128, 8), dtype=np.float64)
+            chr_f = np.empty((128, 8), dtype=np.float64)
+            for j in range(8):
+                chl_f[:, j] = frame_f_stereo[:, 2 * j + 0]
+                chr_f[:, j] = frame_f_stereo[:, 2 * j + 1]
+        else:
+            chl_f = frame_f_stereo[:, 0:1].astype(np.float64, copy=False)
+            chr_f = frame_f_stereo[:, 1:2].astype(np.float64, copy=False)
+
+        # Level 2: apply TNS per channel
+        chl_f_tns, chl_tns_coeffs = aac_tns(chl_f, frame_type)
+        chr_f_tns, chr_tns_coeffs = aac_tns(chr_f, frame_type)
+
+        aac_seq.append(
+            {
+                "frame_type": frame_type,
+                "win_type": WIN_TYPE,
+                "chl": {"frame_F": chl_f_tns, "tns_coeffs": chl_tns_coeffs},
+                "chr": {"frame_F": chr_f_tns, "tns_coeffs": chr_tns_coeffs},
+            }
+        )
+
+        prev_frame_type = frame_type
+
+    return aac_seq

source/level_1/core/aac_configuration.py

@@ -17,6 +17,15 @@ from __future__ import annotations
 # Imports
 from core.aac_types import WinType
 
+# Filterbank
+# ------------------------------------------------------------
 # Window type
 # Options: "SIN", "KBD"
 WIN_TYPE: WinType = "SIN"
+
+
+# TNS
+# ------------------------------------------------------------
+PRED_ORDER = 4
+QUANT_STEP = 0.1
+QUANT_MAX = 0.7  # 4-bit symmetric with step 0.1 -> clamp to [-0.7, +0.7]

source/level_1/core/aac_decoder.py

@@ -28,6 +28,7 @@ from typing import Union
 import soundfile as sf
 
 from core.aac_filterbank import aac_i_filter_bank
+from core.aac_tns import aac_i_tns
 from core.aac_types import *
 
 
@@ -164,3 +165,93 @@ def aac_decoder_1(aac_seq_1: AACSeq1, filename_out: Union[str, Path]) -> StereoS
     sf.write(str(filename_out), y, 48000)
 
     return y
+
+
+def aac_decoder_2(aac_seq_2: AACSeq2, filename_out: Union[str, Path]) -> StereoSignal:
+    """
+    Level-2 AAC decoder (inverse of aac_coder_2).
+
+    Behavior matches Level 1 decoder pipeline, with additional iTNS stage:
+    - Per frame/channel: inverse TNS using stored coefficients
+    - Re-pack to stereo frame_F
+    - IMDCT + windowing
+    - Overlap-add over frames
+    - Remove Level-1 padding (hop samples start/end)
+    - Write output WAV (48 kHz)
+
+    Parameters
+    ----------
+    aac_seq_2 : AACSeq2
+        Encoded sequence as produced by aac_coder_2().
+    filename_out : Union[str, Path]
+        Output WAV filename.
+
+    Returns
+    -------
+    StereoSignal
+        Decoded audio samples (time-domain), stereo, shape (N, 2), dtype float64.
+    """
+    filename_out = Path(filename_out)
+
+    hop = 1024
+    win = 2048
+    K = len(aac_seq_2)
+
+    if K <= 0:
+        raise ValueError("aac_seq_2 must contain at least one frame.")
+
+    n_pad = (K - 1) * hop + win
+    y_pad = np.zeros((n_pad, 2), dtype=np.float64)
+
+    for i, fr in enumerate(aac_seq_2):
+        frame_type: FrameType = fr["frame_type"]
+        win_type: WinType = fr["win_type"]
+
+        chl_f_tns = np.asarray(fr["chl"]["frame_F"], dtype=np.float64)
+        chr_f_tns = np.asarray(fr["chr"]["frame_F"], dtype=np.float64)
+
+        chl_coeffs = np.asarray(fr["chl"]["tns_coeffs"], dtype=np.float64)
+        chr_coeffs = np.asarray(fr["chr"]["tns_coeffs"], dtype=np.float64)
+
+        # Inverse TNS per channel
+        chl_f = aac_i_tns(chl_f_tns, frame_type, chl_coeffs)
+        chr_f = aac_i_tns(chr_f_tns, frame_type, chr_coeffs)
+
+        # Re-pack to the stereo container expected by aac_i_filter_bank
+        if frame_type == "ESH":
+            if chl_f.shape != (128, 8) or chr_f.shape != (128, 8):
+                raise ValueError("ESH channel frame_F must have shape (128, 8).")
+
+            frame_f: FrameF = np.empty((128, 16), dtype=np.float64)
+            for j in range(8):
+                frame_f[:, 2 * j + 0] = chl_f[:, j]
+                frame_f[:, 2 * j + 1] = chr_f[:, j]
+        else:
+            # Accept either (1024,1) or (1024,) from your internal convention.
+            if chl_f.shape == (1024,):
+                chl_col = chl_f.reshape(1024, 1)
+            elif chl_f.shape == (1024, 1):
+                chl_col = chl_f
+            else:
+                raise ValueError("Non-ESH left channel frame_F must be shape (1024,) or (1024, 1).")
+
+            if chr_f.shape == (1024,):
+                chr_col = chr_f.reshape(1024, 1)
+            elif chr_f.shape == (1024, 1):
+                chr_col = chr_f
+            else:
+                raise ValueError("Non-ESH right channel frame_F must be shape (1024,) or (1024, 1).")
+
+            frame_f = np.empty((1024, 2), dtype=np.float64)
+            frame_f[:, 0] = chl_col[:, 0]
+            frame_f[:, 1] = chr_col[:, 0]
+
+        frame_t_hat: FrameT = aac_i_filter_bank(frame_f, frame_type, win_type)
+
+        start = i * hop
+        y_pad[start : start + win, :] += frame_t_hat
+
+    y = aac_remove_padding(y_pad, hop=hop)
+
+    sf.write(str(filename_out), y, 48000)
+    return y

source/level_1/core/aac_snr_db.py

@@ -0,0 +1,60 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - SNR dB calculator
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   This module implements SNR calculation in dB
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from core.aac_types import StereoSignal
+import numpy as np
+
+def snr_db(x_ref: StereoSignal, x_hat: StereoSignal) -> float:
+    """
+    Compute overall SNR (dB) over all samples and channels after aligning lengths.
+
+    Parameters
+    ----------
+    x_ref : StereoSignal
+        Reference stereo stream.
+    x_hat : StereoSignal
+        Reconstructed stereo stream.
+
+    Returns
+    -------
+    float
+        SNR in dB.
+        - Returns +inf if noise power is zero.
+        - Returns -inf if signal power is zero.
+    """
+    x_ref = np.asarray(x_ref, dtype=np.float64)
+    x_hat = np.asarray(x_hat, dtype=np.float64)
+
+    if x_ref.ndim == 1:
+        x_ref = x_ref.reshape(-1, 1)
+    if x_hat.ndim == 1:
+        x_hat = x_hat.reshape(-1, 1)
+
+    n = min(x_ref.shape[0], x_hat.shape[0])
+    c = min(x_ref.shape[1], x_hat.shape[1])
+
+    x_ref = x_ref[:n, :c]
+    x_hat = x_hat[:n, :c]
+
+    err = x_ref - x_hat
+    ps = float(np.sum(x_ref * x_ref))
+    pn = float(np.sum(err * err))
+
+    if pn <= 0.0:
+        return float("inf")
+    if ps <= 0.0:
+        return float("-inf")
+
+    return float(10.0 * np.log10(ps / pn))

source/level_1/core/aac_types.py

@@ -10,7 +10,6 @@
 #
 # Description:
 #   This module implements Public Type aliases
-#
 # ------------------------------------------------------------
 from __future__ import annotations
 
@@ -39,7 +38,7 @@ Window type codes (AAC):
 """
 
 ChannelKey: TypeAlias = Literal["chl", "chr"]
-"""Channel dictionary keys used in Level 1 payloads."""
+"""Channel dictionary keys used in Level payloads."""
 
 
 # -----------------------------------------------------------------------------
@@ -105,6 +104,40 @@ Examples:
 dtype: float64
 """
 
+MdctFrameChannel: TypeAlias = FloatArray
+"""
+Per-channel MDCT container used in Level-1/2 sequences.
+
+Typical shapes:
+- If frame_type in {"OLS","LSS","LPS"}: (1024, 1) or (1024,)
+- If frame_type == "ESH": (128, 8)  (8 short subframes for one channel)
+
+dtype: float64
+
+Notes
+-----
+Some parts of the assignment store long-frame coefficients as a column vector
+(1024, 1) to match MATLAB conventions. Internally you may also use (1024,)
+when convenient, but the semantic meaning is identical.
+"""
+
+TnsCoeffs: TypeAlias = FloatArray
+"""
+Quantized TNS predictor coefficients (one channel).
+
+Typical shapes (Level 2):
+- If frame_type == "ESH": (4, 8)   (order p=4 for each of the 8 short subframes)
+- Else: (4, 1)            (order p=4 for the long frame)
+
+dtype: float64
+
+Notes
+-----
+The assignment uses a 4-bit uniform symmetric quantizer with step size 0.1.
+We store the quantized coefficient values as float64 (typically multiples of 0.1)
+to keep the pipeline simple and readable.
+"""
+
 
 FrameT: TypeAlias = FloatArray
 """
@@ -142,17 +175,23 @@ Rationale for ESH (128, 16):
 dtype: float64
 """
 
-FrameChannelF: TypeAlias = FloatArray
+FrameChannelF: TypeAlias = MdctFrameChannel
 """
-Frequency-domain single-channel frame (MDCT coefficients).
+Frequency-domain single-channel MDCT coefficients.
 
-Typical shapes (Level 1):
+Typical shapes (Level 1/2):
-- If frame_type in {"OLS","LSS","LPS"}: (1024,)
+- If frame_type in {"OLS","LSS","LPS"}: (1024, 1) or (1024,)
-- If frame_type == "ESH": (128, 8)  (8 short subframes for one channel)
+- If frame_type == "ESH": (128, 8)
 
 dtype: float64
 """
 
+BandRanges: TypeAlias = list[tuple[int, int]]
+"""
+Bark-band index ranges [start, end] (inclusive) for MDCT lines.
+
+Used by TNS to map MDCT indices k to Bark bands.
+"""
 
 # -----------------------------------------------------------------------------
 # Level 1 AAC sequence payload types
@@ -168,7 +207,7 @@ class AACChannelFrameF(TypedDict):
         The MDCT coefficients for ONE channel.
         Typical shapes:
         - ESH: (128, 8)   (8 short subframes)
-        - else: (1024, )
+        - else: (1024, 1) or (1024,)
     """
     frame_F: FrameChannelF
 
@@ -191,3 +230,53 @@ List of length K (K = number of frames).
 Each element is a dict with keys:
 - "frame_type", "win_type", "chl", "chr"
 """
+
+
+# -----------------------------------------------------------------------------
+# Level 2 AAC sequence payload types (TNS)
+# -----------------------------------------------------------------------------
+
+class AACChannelFrameF2(TypedDict):
+    """
+    Per-channel payload for aac_seq_2[i]["chl"] or ["chr"] (Level 2).
+
+    Keys
+    ----
+    frame_F:
+        The TNS-processed MDCT coefficients for ONE channel.
+        Typical shapes:
+        - ESH: (128, 8)
+        - else: (1024, 1) or (1024,)
+    tns_coeffs:
+        Quantized TNS predictor coefficients for ONE channel.
+        Typical shapes:
+        - ESH: (PRED_ORDER, 8)
+        - else: (PRED_ORDER, 1)
+    """
+    frame_F: FrameChannelF
+    tns_coeffs: TnsCoeffs
+
+
+class AACSeq2Frame(TypedDict):
+    """
+    One frame dictionary element of aac_seq_2 (Level 2).
+    """
+    frame_type: FrameType
+    win_type: WinType
+    chl: AACChannelFrameF2
+    chr: AACChannelFrameF2
+
+
+AACSeq2: TypeAlias = List[AACSeq2Frame]
+"""
+AAC sequence for Level 2:
+List of length K (K = number of frames).
+
+Each element is a dict with keys:
+- "frame_type", "win_type", "chl", "chr"
+
+Level 2 adds:
+- per-channel "tns_coeffs"
+and stores:
+- per-channel "frame_F" after applying TNS.
+"""

source/level_1/core/tests/test_SSC.py

@@ -1,234 +0,0 @@
-# ------------------------------------------------------------
-# AAC Coder/Decoder - Sequence Segmentation Control Tests
-#
-# Multimedia course at Aristotle University of
-# Thessaloniki (AUTh)
-#
-# Author:
-#   Christos Choutouridis (ΑΕΜ 8997)
-#   cchoutou@ece.auth.gr
-#
-# Description:
-#   Tests for Sequence Segmentation Control module (SSC).
-# ------------------------------------------------------------
-
-from __future__ import annotations
-
-import numpy as np
-
-from core.aac_ssc import aac_SSC
-from core.aac_types import FrameT
-
-# -----------------------------------------------------------------------------
-# Helper fixtures for SSC
-# -----------------------------------------------------------------------------
-
-def _next_frame_no_attack() -> FrameT:
-    """
-    Build a next_frame_T that must NOT trigger ESH detection.
-
-    Uses exact zeros so all segment energies are zero and the condition
-    s[l] > 1e-3 cannot hold for any l.
-    """
-    return np.zeros((2048, 2), dtype=np.float64)
-
-
-def _next_frame_strong_attack(
-    *,
-    attack_left: bool,
-    attack_right: bool,
-    segment_l: int = 4,
-    baseline: float = 1e-6,
-    burst_amp: float = 1.0,
-) -> FrameT:
-    """
-    Build a next_frame_T (2048x2) that should trigger ESH detection on selected channels.
-
-    Attack criterion (spec):
-      Attack exists if there exists l in {1..7} such that:
-        s[l] > 1e-3  and  ds[l] > 10,
-      where s[l] is the energy of segment l (length 128) after high-pass filtering,
-      and ds[l] = s[l] / s[l-1].
-
-    Construction:
-    - A small baseline is added everywhere to avoid relying on the epsilon guard in ds,
-      keeping ds behavior stable/reproducible.
-    - A strong burst is added inside a chosen segment l in 1..7.
-    """
-    if not (1 <= segment_l <= 7):
-        raise ValueError(f"segment_l must be in [1, 7], got {segment_l}.")
-
-    x = np.full((2048, 2), baseline, dtype=np.float64)
-
-    a = segment_l * 128
-    b = (segment_l + 1) * 128
-
-    if attack_left:
-        x[a:b, 0] += burst_amp
-    if attack_right:
-        x[a:b, 1] += burst_amp
-
-    return x
-
-
-def _next_frame_below_s_threshold(
-    *,
-    left: bool,
-    right: bool,
-    segment_l: int = 4,
-    impulse_amp: float = 0.01,
-) -> FrameT:
-    """
-    Construct a next_frame_T where s[l] is below 1e-3, so ESH must NOT be triggered,
-    even if the ratio ds[l] could be large.
-
-    We place a single impulse of amplitude 'impulse_amp' inside one segment.
-    Approx. segment energy: s[l] ~= impulse_amp^2.
-
-    Example:
-      impulse_amp = 0.01 => s[l] ~= 1e-4 < 1e-3
-    """
-    if not (1 <= segment_l <= 7):
-        raise ValueError(f"segment_l must be in [1, 7], got {segment_l}.")
-
-    x = np.zeros((2048, 2), dtype=np.float64)
-
-    idx = segment_l * 128 + 10  # inside segment l
-    if left:
-        x[idx, 0] = impulse_amp
-    if right:
-        x[idx, 1] = impulse_amp
-
-    return x
-
-
-# -----------------------------------------------------------------------------
-# 1) Fixed/mandatory cases (prev frame type forces current type)
-# -----------------------------------------------------------------------------
-
-def test_ssc_fixed_cases_prev_lss_and_lps() -> None:
-    """
-    Spec:
-      - If prev was LSS => current MUST be ESH
-      - If prev was LPS => current MUST be OLS
-    independent of attack detection on (i+1).
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-
-    next_attack = _next_frame_strong_attack(attack_left=True, attack_right=True)
-
-    out1 = aac_SSC(frame_t, next_attack, "LSS")
-    assert out1 == "ESH"
-
-    out2 = aac_SSC(frame_t, next_attack, "LPS")
-    assert out2 == "OLS"
-
-
-# -----------------------------------------------------------------------------
-# 2) Cases requiring next-frame ESH prediction (attack computation)
-# -----------------------------------------------------------------------------
-
-def test_prev_ols_next_not_esh_returns_ols() -> None:
-    """
-    If prev=OLS, current is:
-      - LSS iff (i+1) is predicted ESH
-      - else OLS
-    Here: no attack => expect OLS.
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    next_t = _next_frame_no_attack()
-
-    out = aac_SSC(frame_t, next_t, "OLS")
-    assert out == "OLS"
-
-
-def test_prev_ols_next_esh_both_channels_returns_lss() -> None:
-    """
-    prev=OLS and next predicted ESH for both channels:
-      per-channel: LSS, LSS
-      merged: LSS
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    next_t = _next_frame_strong_attack(attack_left=True, attack_right=True)
-
-    out = aac_SSC(frame_t, next_t, "OLS")
-    assert out == "LSS"
-
-
-def test_prev_ols_next_esh_one_channel_returns_lss() -> None:
-    """
-    prev=OLS:
-      - one channel predicts ESH => LSS
-      - other channel predicts not ESH => OLS
-    Merge table: OLS + LSS => LSS (either side).
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-
-    next1_t = _next_frame_strong_attack(attack_left=True, attack_right=False)
-    out1 = aac_SSC(frame_t, next1_t, "OLS")
-    assert out1 == "LSS"
-
-    next2_t = _next_frame_strong_attack(attack_left=False, attack_right=True)
-    out2 = aac_SSC(frame_t, next2_t, "OLS")
-    assert out2 == "LSS"
-
-
-def test_prev_esh_next_esh_both_channels_returns_esh() -> None:
-    """
-    prev=ESH and next predicted ESH for both channels:
-      per-channel: ESH, ESH
-      merged: ESH
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    next_t = _next_frame_strong_attack(attack_left=True, attack_right=True)
-
-    out = aac_SSC(frame_t, next_t, "ESH")
-    assert out == "ESH"
-
-
-def test_prev_esh_next_not_esh_both_channels_returns_lps() -> None:
-    """
-    prev=ESH and next not predicted ESH for both channels:
-      per-channel: LPS, LPS
-      merged: LPS
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    next_t = _next_frame_no_attack()
-
-    out = aac_SSC(frame_t, next_t, "ESH")
-    assert out == "LPS"
-
-
-def test_prev_esh_next_esh_one_channel_merged_is_esh() -> None:
-    """
-    prev=ESH:
-      - one channel predicts ESH => ESH
-      - other channel predicts not ESH => LPS
-    Merge table: ESH + LPS => ESH (either side).
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-
-    next1_t = _next_frame_strong_attack(attack_left=True, attack_right=False)
-    out1 = aac_SSC(frame_t, next1_t, "ESH")
-    assert out1 == "ESH"
-
-    next2_t = _next_frame_strong_attack(attack_left=False, attack_right=True)
-    out2 = aac_SSC(frame_t, next2_t, "ESH")
-    assert out2 == "ESH"
-
-
-def test_threshold_s_must_exceed_1e_3() -> None:
-    """
-    Spec: next frame is predicted ESH only if:
-      s[l] > 1e-3  AND  ds[l] > 10
-    for some l in 1..7.
-
-    This test checks the necessity of the s[l] threshold:
-      - Create a frame with s[l] ~= 1e-4 < 1e-3 (single impulse with amp 0.01).
-      - Expect: not classified as ESH -> for prev=OLS return OLS.
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    next_t = _next_frame_below_s_threshold(left=True, right=True, impulse_amp=0.01)
-
-    out = aac_SSC(frame_t, next_t, "OLS")
-    assert out == "OLS"

source/level_1/core/tests/test_aac_coder_decoder.py

@@ -1,156 +0,0 @@
-# ------------------------------------------------------------
-# AAC Coder/Decoder - AAC Coder/DecoderTests
-#
-# Multimedia course at Aristotle University of
-# Thessaloniki (AUTh)
-#
-# Author:
-#   Christos Choutouridis (ΑΕΜ 8997)
-#   cchoutou@ece.auth.gr
-#
-# Description:
-#   Tests for AAC Coder/Decoder module.
-# ------------------------------------------------------------
-from __future__ import annotations
-
-from pathlib import Path
-
-import numpy as np
-import pytest
-import soundfile as sf
-
-from core.aac_coder import aac_coder_1
-from core.aac_decoder import aac_decoder_1
-from core.aac_types import *
-
-
-# Helper "fixtures" for aac_coder_1 / i_aac_coder_1
-# -----------------------------------------------------------------------------
-
-def _snr_db(x_ref: StereoSignal, x_hat: StereoSignal) -> float:
-    """
-    Compute overall SNR (dB) over all samples and channels after aligning lengths.
-
-    Parameters
-    ----------
-    x_ref : StereoSignal
-        Reference signal, shape (N, 2) typical.
-    x_hat : StereoSignal
-        Reconstructed signal, shape (M, 2) typical.
-
-    Returns
-    -------
-    float
-        SNR in dB.
-        - Returns +inf if noise power is zero.
-        - Returns -inf if signal power is zero.
-    """
-    x_ref = np.asarray(x_ref, dtype=np.float64)
-    x_hat = np.asarray(x_hat, dtype=np.float64)
-
-    # Be conservative: align lengths and common channels.
-    if x_ref.ndim == 1:
-        x_ref = x_ref.reshape(-1, 1)
-    if x_hat.ndim == 1:
-        x_hat = x_hat.reshape(-1, 1)
-
-    n = min(x_ref.shape[0], x_hat.shape[0])
-    c = min(x_ref.shape[1], x_hat.shape[1])
-
-    x_ref = x_ref[:n, :c]
-    x_hat = x_hat[:n, :c]
-
-    err = x_ref - x_hat
-    ps = float(np.sum(x_ref * x_ref))
-    pn = float(np.sum(err * err))
-
-    if pn <= 0.0:
-        return float("inf")
-    if ps <= 0.0:
-        return float("-inf")
-
-    return float(10.0 * np.log10(ps / pn))
-
-
-@pytest.fixture()
-def tmp_stereo_wav(tmp_path: Path) -> Path:
-    """
-    Create a temporary 48 kHz stereo WAV with random samples.
-    """
-    rng = np.random.default_rng(123)
-    fs = 48000
-
-    # ~1 second of audio (kept small for test speed).
-    n = fs
-    x: StereoSignal = rng.normal(size=(n, 2)).astype(np.float64)
-
-    wav_path = tmp_path / "in.wav"
-    sf.write(str(wav_path), x, fs)
-    return wav_path
-
-
-def test_aac_coder_seq_schema_and_shapes(tmp_stereo_wav: Path) -> None:
-    """
-    Module-level contract test:
-    Ensure aac_seq_1 follows the expected schema and per-frame shapes.
-    """
-    aac_seq: AACSeq1 = aac_coder_1(tmp_stereo_wav)
-
-    assert isinstance(aac_seq, list)
-    assert len(aac_seq) > 0
-
-    for fr in aac_seq:
-        assert isinstance(fr, dict)
-
-        # Required keys
-        assert "frame_type" in fr
-        assert "win_type" in fr
-        assert "chl" in fr
-        assert "chr" in fr
-
-        frame_type = fr["frame_type"]
-        win_type = fr["win_type"]
-
-        assert frame_type in ("OLS", "LSS", "ESH", "LPS")
-        assert win_type in ("SIN", "KBD")
-
-        assert isinstance(fr["chl"], dict)
-        assert isinstance(fr["chr"], dict)
-        assert "frame_F" in fr["chl"]
-        assert "frame_F" in fr["chr"]
-
-        chl_f = np.asarray(fr["chl"]["frame_F"], dtype=np.float64)
-        chr_f = np.asarray(fr["chr"]["frame_F"], dtype=np.float64)
-
-        if frame_type == "ESH":
-            assert chl_f.shape == (128, 8)
-            assert chr_f.shape == (128, 8)
-        else:
-            assert chl_f.shape == (1024, 1)
-            assert chr_f.shape == (1024, 1)
-
-
-def test_end_to_end_aac_coder_decoder_high_snr(tmp_stereo_wav: Path, tmp_path: Path) -> None:
-    """
-    End-to-end test:
-    Encode + decode and check SNR is very high (numerical-noise only).
-
-    The threshold is intentionally loose to avoid fragility across platforms/BLAS.
-    """
-    x_ref, fs = sf.read(str(tmp_stereo_wav), always_2d=True)
-    x_ref = np.asarray(x_ref, dtype=np.float64)
-    assert int(fs) == 48000
-
-    out_wav = tmp_path / "out.wav"
-
-    aac_seq = aac_coder_1(tmp_stereo_wav)
-    x_hat: StereoSignal = aac_decoder_1(aac_seq, out_wav)
-
-    # Basic sanity: output file exists and is readable
-    assert out_wav.exists()
-    x_hat_file, fs_hat = sf.read(str(out_wav), always_2d=True)
-    assert int(fs_hat) == 48000
-
-    # SNR against returned array (file should match closely, but we do not require it here).
-    snr = _snr_db(x_ref, x_hat)
-    assert snr > 80.0

source/level_1/core/tests/test_filterbank.py

@@ -1,269 +0,0 @@
-# ------------------------------------------------------------
-# AAC Coder/Decoder - Filterbank Tests
-#
-# Multimedia course at Aristotle University of
-# Thessaloniki (AUTh)
-#
-# Author:
-#   Christos Choutouridis (ΑΕΜ 8997)
-#   cchoutou@ece.auth.gr
-#
-# Description:
-#   Tests for Filterbank module.
-# ------------------------------------------------------------
-from __future__ import annotations
-
-from typing import Sequence
-import pytest
-
-from core.aac_filterbank import aac_filter_bank, aac_i_filter_bank
-from core.aac_types import *
-
-# Helper fixtures for filterbank
-# -----------------------------------------------------------------------------
-
-def _ola_reconstruct(x: StereoSignal, frame_types: Sequence[FrameType], win_type: WinType) -> StereoSignal:
-    """
-    Analyze-synthesize each frame and overlap-add with hop=1024.
-
-    Parameters
-    ----------
-    x : StereoSignal
-        Input stereo stream, expected shape (N, 2).
-    frame_types : Sequence[FrameType]
-        Length K sequence of frame types for frames starting at i*1024.
-    win_type : WinType
-        Window type ("SIN" or "KBD").
-
-    Returns
-    -------
-    StereoSignal
-        Reconstructed stereo stream, same shape as x (N, 2).
-    """
-    hop = 1024
-    win = 2048
-    K = len(frame_types)
-
-    y: StereoSignal = np.zeros_like(x, dtype=np.float64)
-
-    for i in range(K):
-        start = i * hop
-        frame_t: FrameT = x[start:start + win, :]
-        frame_f: FrameF = aac_filter_bank(frame_t, frame_types[i], win_type)
-        frame_t_hat: FrameT = aac_i_filter_bank(frame_f, frame_types[i], win_type)
-        y[start:start + win, :] += frame_t_hat
-
-    return y
-
-
-def _snr_db(x: StereoSignal, y: StereoSignal) -> float:
-    """
-    Compute SNR in dB over all samples/channels.
-    """
-    err = x - y
-    ps = float(np.sum(x * x))
-    pn = float(np.sum(err * err))
-    if pn <= 0.0:
-        return float("inf")
-    if ps <= 0.0:
-        return float("-inf")
-    return 10.0 * float(np.log10(ps / pn))
-
-
-# -----------------------------------------------------------------------------
-# Forward filterbank tests
-# -----------------------------------------------------------------------------
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-@pytest.mark.parametrize("frame_type", ["OLS", "LSS", "LPS"])
-def test_filterbank_shapes_long_sequences(frame_type: FrameType, win_type: WinType) -> None:
-    """
-    Contract test: for OLS/LSS/LPS, aac_filter_bank returns shape (1024, 2).
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    frame_f = aac_filter_bank(frame_t, frame_type, win_type)
-    assert frame_f.shape == (1024, 2)
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_filterbank_shapes_esh(win_type: WinType) -> None:
-    """
-    Contract test: for ESH, aac_filter_bank returns shape (128, 16).
-    """
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    frame_f = aac_filter_bank(frame_t, "ESH", win_type)
-    assert frame_f.shape == (128, 16)
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_filterbank_channel_isolation_long_sequences(win_type: WinType) -> None:
-    """
-    Behavior test: for OLS (representative long-sequence), channels are independent.
-    If right channel is zero and left is random, right spectrum should be near zero.
-    """
-    rng = np.random.default_rng(0)
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    frame_t[:, 0] = rng.normal(size=2048)
-
-    frame_f = aac_filter_bank(frame_t, "OLS", win_type)
-
-    assert np.max(np.abs(frame_f[:, 1])) < 1e-9
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_filterbank_channel_isolation_esh(win_type: WinType) -> None:
-    """
-    Behavior test: for ESH, channels are independent.
-    If right channel is zero and left is random, all odd columns (right) should be near zero.
-    """
-    rng = np.random.default_rng(1)
-    frame_t: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    frame_t[:, 0] = rng.normal(size=2048)
-
-    frame_f = aac_filter_bank(frame_t, "ESH", win_type)
-
-    right_cols = frame_f[:, 1::2]  # columns 1,3,5,...,15
-    assert np.max(np.abs(right_cols)) < 1e-9
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_filterbank_esh_ignores_outer_regions(win_type: WinType) -> None:
-    """
-    Spec-driven behavior test:
-    ESH uses only the central region [448, 1600), split into 8 overlapping
-    windows of length 256 with 50% overlap.
-
-    Therefore, changing samples outside [448, 1600) must not affect the output.
-    """
-    rng = np.random.default_rng(2)
-
-    frame_a: FrameT = np.zeros((2048, 2), dtype=np.float64)
-    frame_b: FrameT = np.zeros((2048, 2), dtype=np.float64)
-
-    center = rng.normal(size=(1152, 2))
-    frame_a[448:1600, :] = center
-    frame_b[448:1600, :] = center
-
-    frame_b[0:448, :] = rng.normal(size=(448, 2))
-    frame_b[1600:2048, :] = rng.normal(size=(448, 2))
-
-    fa = aac_filter_bank(frame_a, "ESH", win_type)
-    fb = aac_filter_bank(frame_b, "ESH", win_type)
-
-    # Use a tiny tolerance to avoid flaky failures due to floating-point minutiae.
-    np.testing.assert_allclose(fa, fb, rtol=0.0, atol=1e-12)
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_filterbank_output_is_finite(win_type: WinType) -> None:
-    """
-    Sanity test: output must not contain NaN or inf for representative cases.
-    """
-    rng = np.random.default_rng(3)
-    frame_t: FrameT = rng.normal(size=(2048, 2)).astype(np.float64)
-
-    for frame_type in ("OLS", "LSS", "ESH", "LPS"):
-        frame_f = aac_filter_bank(frame_t, frame_type, win_type)
-        assert np.isfinite(frame_f).all()
-
-
-# -----------------------------------------------------------------------------
-# Reverse i_filterbank tests
-# -----------------------------------------------------------------------------
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_ifilterbank_shapes_long_sequences(win_type: WinType) -> None:
-    """
-    Contract test: for OLS/LSS/LPS, aac_i_filter_bank returns shape (2048, 2).
-    """
-    frame_f: FrameF = np.zeros((1024, 2), dtype=np.float64)
-    for frame_type in ("OLS", "LSS", "LPS"):
-        frame_t = aac_i_filter_bank(frame_f, frame_type, win_type)
-        assert frame_t.shape == (2048, 2)
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_ifilterbank_shapes_esh(win_type: WinType) -> None:
-    """
-    Contract test: for ESH, aac_i_filter_bank returns shape (2048, 2).
-    """
-    frame_f: FrameF = np.zeros((128, 16), dtype=np.float64)
-    frame_t = aac_i_filter_bank(frame_f, "ESH", win_type)
-    assert frame_t.shape == (2048, 2)
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_roundtrip_per_frame_is_finite(win_type: WinType) -> None:
-    """
-    Sanity test: per-frame analysis+synthesis must produce finite outputs.
-    """
-    rng = np.random.default_rng(0)
-    frame_t: FrameT = rng.normal(size=(2048, 2)).astype(np.float64)
-
-    for frame_type in ("OLS", "LSS", "ESH", "LPS"):
-        frame_f = aac_filter_bank(frame_t, frame_type, win_type)
-        frame_t_hat = aac_i_filter_bank(frame_f, frame_type, win_type)
-        assert np.isfinite(frame_t_hat).all()
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_ola_reconstruction_ols_high_snr(win_type: WinType) -> None:
-    """
-    Module-level test:
-    OLS analysis+synthesis with hop=1024 must reconstruct with high SNR
-    in the steady-state region.
-    """
-    rng = np.random.default_rng(1)
-
-    K = 6
-    N = 1024 * (K + 1)
-    x: StereoSignal = rng.normal(size=(N, 2)).astype(np.float64)
-
-    y = _ola_reconstruct(x, ["OLS"] * K, win_type)
-
-    a = 1024
-    b = N - 1024
-    snr = _snr_db(x[a:b, :], y[a:b, :])
-    assert snr > 50.0
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_ola_reconstruction_esh_high_snr(win_type: WinType) -> None:
-    """
-    Module-level test:
-    ESH analysis+synthesis with hop=1024 must reconstruct with high SNR
-    in the steady-state region.
-    """
-    rng = np.random.default_rng(2)
-
-    K = 6
-    N = 1024 * (K + 1)
-    x: StereoSignal = rng.normal(size=(N, 2)).astype(np.float64)
-
-    y = _ola_reconstruct(x, ["ESH"] * K, win_type)
-
-    a = 1024
-    b = N - 1024
-    snr = _snr_db(x[a:b, :], y[a:b, :])
-    assert snr > 45.0
-
-
-@pytest.mark.parametrize("win_type", ["SIN", "KBD"])
-def test_ola_reconstruction_transition_sequence(win_type: WinType) -> None:
-    """
-    Transition sequence test matching the windowing logic:
-      OLS -> LSS -> ESH -> LPS -> OLS -> OLS
-    """
-    rng = np.random.default_rng(3)
-
-    frame_types: list[FrameType] = ["OLS", "LSS", "ESH", "LPS", "OLS", "OLS"]
-    K = len(frame_types)
-    N = 1024 * (K + 1)
-    x: StereoSignal = rng.normal(size=(N, 2)).astype(np.float64)
-
-    y = _ola_reconstruct(x, frame_types, win_type)
-
-    a = 1024
-    b = N - 1024
-    snr = _snr_db(x[a:b, :], y[a:b, :])
-    assert snr > 40.0

source/level_1/core/tests/test_filterbank_internal.py

@@ -1,117 +0,0 @@
-# ------------------------------------------------------------
-# AAC Coder/Decoder - Filterbank internal (mdct) Tests
-#
-# Multimedia course at Aristotle University of
-# Thessaloniki (AUTh)
-#
-# Author:
-#   Christos Choutouridis (ΑΕΜ 8997)
-#   cchoutou@ece.auth.gr
-#
-# Description:
-#   Tests for Filterbank internal MDCT/IMDCT functionality.
-# ------------------------------------------------------------
-from __future__ import annotations
-
-import numpy as np
-import pytest
-
-from core.aac_filterbank import _imdct, _mdct
-from core.aac_types import FloatArray, TimeSignal, MdctCoeffs
-
-
-def _assert_allclose(a: FloatArray, b: FloatArray, *, rtol: float, atol: float) -> None:
-    """
-    Helper for consistent tolerances across tests.
-    """
-    np.testing.assert_allclose(a, b, rtol=rtol, atol=atol)
-
-
-def _estimate_gain(y: MdctCoeffs, x: MdctCoeffs) -> float:
-    """
-    Estimate scalar gain g such that y ~= g*x in least-squares sense.
-    """
-    denom = float(np.dot(x, x))
-    if denom == 0.0:
-        return 0.0
-    return float(np.dot(y, x) / denom)
-
-
-tolerance = 1e-10
-
-@pytest.mark.parametrize("N", [256, 2048])
-def test_mdct_imdct_mdct_identity_up_to_gain(N: int) -> None:
-    """
-    Consistency test in coefficient domain:
-      mdct(imdct(X)) ~= g * X
-
-    For the chosen (non-orthonormal) scaling, g is expected to be close to 2.
-    """
-    rng = np.random.default_rng(0)
-    K = N // 2
-
-    X: MdctCoeffs = rng.normal(size=K).astype(np.float64)
-    x: TimeSignal = _imdct(X)
-    X_hat: MdctCoeffs = _mdct(x)
-
-    g = _estimate_gain(X_hat, X)
-    _assert_allclose(X_hat, g * X, rtol=tolerance, atol=tolerance)
-    _assert_allclose(np.array([g], dtype=np.float64), np.array([2.0], dtype=np.float64), rtol=tolerance, atol=tolerance)
-
-
-@pytest.mark.parametrize("N", [256, 2048])
-def test_mdct_linearity(N: int) -> None:
-    """
-    Linearity test:
-      mdct(a*x + b*y) == a*mdct(x) + b*mdct(y)
-    """
-    rng = np.random.default_rng(1)
-    x: TimeSignal = rng.normal(size=N).astype(np.float64)
-    y: TimeSignal = rng.normal(size=N).astype(np.float64)
-
-    a = 0.37
-    b = -1.12
-
-    left: MdctCoeffs = _mdct(a * x + b * y)
-    right: MdctCoeffs = a * _mdct(x) + b * _mdct(y)
-
-    _assert_allclose(left, right, rtol=tolerance, atol=tolerance)
-
-
-@pytest.mark.parametrize("N", [256, 2048])
-def test_imdct_linearity(N: int) -> None:
-    """
-    Linearity test for IMDCT:
-      imdct(a*X + b*Y) == a*imdct(X) + b*imdct(Y)
-    """
-    rng = np.random.default_rng(2)
-    K = N // 2
-
-    X: MdctCoeffs = rng.normal(size=K).astype(np.float64)
-    Y: MdctCoeffs = rng.normal(size=K).astype(np.float64)
-
-    a = -0.5
-    b = 2.0
-
-    left: TimeSignal = _imdct(a * X + b * Y)
-    right: TimeSignal = a * _imdct(X) + b * _imdct(Y)
-
-    _assert_allclose(left, right, rtol=tolerance, atol=tolerance)
-
-
-@pytest.mark.parametrize("N", [256, 2048])
-def test_mdct_imdct_outputs_are_finite(N: int) -> None:
-    """
-    Sanity test: no NaN/inf on random inputs.
-    """
-    rng = np.random.default_rng(3)
-    K = N // 2
-
-    x: TimeSignal = rng.normal(size=N).astype(np.float64)
-    X: MdctCoeffs = rng.normal(size=K).astype(np.float64)
-
-    X1 = _mdct(x)
-    x1 = _imdct(X)
-
-    assert np.isfinite(X1).all()
-    assert np.isfinite(x1).all()

source/level_1/level_1.py

@@ -22,13 +22,13 @@ from __future__ import annotations
 from pathlib import Path
 from typing import Union
 
-import numpy as np
 import soundfile as sf
 
 from core.aac_types   import AACSeq1, StereoSignal
 from core.aac_coder   import aac_coder_1 as core_aac_coder_1
 from core.aac_coder   import aac_read_wav_stereo_48k
 from core.aac_decoder import aac_decoder_1 as core_aac_decoder_1
+from core.aac_snr_db  import snr_db
 
 
 # -----------------------------------------------------------------------------
@@ -80,50 +80,6 @@ def aac_decoder_1(aac_seq_1: AACSeq1, filename_out: Union[str, Path]) -> StereoS
 # Demo (Level 1)
 # -----------------------------------------------------------------------------
 
-def _snr_db(x_ref: StereoSignal, x_hat: StereoSignal) -> float:
-    """
-    Compute overall SNR (dB) over all samples and channels after aligning lengths.
-
-    Parameters
-    ----------
-    x_ref : StereoSignal
-        Reference stereo stream.
-    x_hat : StereoSignal
-        Reconstructed stereo stream.
-
-    Returns
-    -------
-    float
-        SNR in dB.
-        - Returns +inf if noise power is zero.
-        - Returns -inf if signal power is zero.
-    """
-    x_ref = np.asarray(x_ref, dtype=np.float64)
-    x_hat = np.asarray(x_hat, dtype=np.float64)
-
-    if x_ref.ndim == 1:
-        x_ref = x_ref.reshape(-1, 1)
-    if x_hat.ndim == 1:
-        x_hat = x_hat.reshape(-1, 1)
-
-    n = min(x_ref.shape[0], x_hat.shape[0])
-    c = min(x_ref.shape[1], x_hat.shape[1])
-
-    x_ref = x_ref[:n, :c]
-    x_hat = x_hat[:n, :c]
-
-    err = x_ref - x_hat
-    ps = float(np.sum(x_ref * x_ref))
-    pn = float(np.sum(err * err))
-
-    if pn <= 0.0:
-        return float("inf")
-    if ps <= 0.0:
-        return float("-inf")
-
-    return float(10.0 * np.log10(ps / pn))
-
-
 def demo_aac_1(filename_in: Union[str, Path], filename_out: Union[str, Path]) -> float:
     """
     Demonstration for the Level-1 codec.
@@ -158,12 +114,11 @@ def demo_aac_1(filename_in: Union[str, Path], filename_out: Union[str, Path]) ->
     x_hat = aac_decoder_1(aac_seq_1, filename_out)
 
     # Optional sanity: ensure output file exists and is readable
-    x_hat_file, fs_hat = sf.read(str(filename_out), always_2d=True)
+    _, fs_hat = sf.read(str(filename_out), always_2d=True)
-    _ = x_hat_file
     if int(fs_hat) != 48000:
         raise ValueError("Decoded output sampling rate must be 48 kHz.")
 
-    return _snr_db(x_ref, x_hat)
+    return snr_db(x_ref, x_hat)
 
 
 # -----------------------------------------------------------------------------
@@ -172,11 +127,14 @@ def demo_aac_1(filename_in: Union[str, Path], filename_out: Union[str, Path]) ->
 
 if __name__ == "__main__":
     # Example:
-    #   python -m level_1.level_1 input.wav output.wav
+    #   cd level_1
+    #   python -m level_1 input.wav output.wav
+    #   or
+    #   python -m level_1 material/LicorDeCalandraca.wav LicorDeCalandraca_out.wav
     import sys
 
     if len(sys.argv) != 3:
-        raise SystemExit("Usage: python -m level_1.level_1 <input.wav> <output.wav>")
+        raise SystemExit("Usage: python -m level_1 <input.wav> <output.wav>")
 
     in_wav = Path(sys.argv[1])
     out_wav = Path(sys.argv[2])

source/level_1/material/huff_utils.py

@@ -0,0 +1,400 @@
+import numpy as np
+import scipy.io as sio
+import os
+
+# ------------------ LOAD LUT ------------------
+
+def load_LUT(mat_filename=None):
+    """
+    Loads the list of Huffman Codebooks (LUTs)
+
+    Returns:
+        huffLUT : list (index 1..11 used, index 0 unused)
+    """
+    if mat_filename is None:
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        mat_filename = os.path.join(current_dir, "huffCodebooks.mat")
+
+    mat = sio.loadmat(mat_filename)
+
+
+    huffCodebooks_raw = mat['huffCodebooks'].squeeze()
+
+    huffCodebooks = []
+    for i in range(11):
+        huffCodebooks.append(np.array(huffCodebooks_raw[i]))
+
+    # Build inverse VLC tables
+    invTable = [None] * 11
+
+    for i in range(11):
+        h = huffCodebooks[i][:, 2].astype(int)       # column 3
+        hlength = huffCodebooks[i][:, 1].astype(int) # column 2
+
+        hbin = []
+        for j in range(len(h)):
+            hbin.append(format(h[j], f'0{hlength[j]}b'))
+
+        invTable[i] = vlc_table(hbin)
+
+    # Build Huffman LUT dicts
+    huffLUT = [None] * 12  # index 0 unused
+    params = [
+        (4, 1, True),
+        (4, 1, True),
+        (4, 2, False),
+        (4, 2, False),
+        (2, 4, True),
+        (2, 4, True),
+        (2, 7, False),
+        (2, 7, False),
+        (2, 12, False),
+        (2, 12, False),
+        (2, 16, False),
+    ]
+
+    for i, (nTupleSize, maxAbs, signed) in enumerate(params, start=1):
+        huffLUT[i] = {
+            'LUT': huffCodebooks[i-1],
+            'invTable': invTable[i-1],
+            'codebook': i,
+            'nTupleSize': nTupleSize,
+            'maxAbsCodeVal': maxAbs,
+            'signedValues': signed
+        }
+
+    return huffLUT
+
+def vlc_table(code_array):
+    """
+    codeArray: list of strings, each string is a Huffman codeword (e.g. '0101')
+    returns:
+        h : NumPy array of shape (num_nodes, 3)
+            columns:
+              [ next_if_0 , next_if_1 , symbol_index ]
+    """
+    h = np.zeros((1, 3), dtype=int)
+
+    for code_index, code in enumerate(code_array, start=1):
+        word = [int(bit) for bit in code]
+        h_index = 0
+
+        for bit in word:
+            k = bit
+            next_node = h[h_index, k]
+            if next_node == 0:
+                h = np.vstack([h, [0, 0, 0]])
+                new_index = h.shape[0] - 1
+                h[h_index, k] = new_index
+                h_index = new_index
+            else:
+                h_index = next_node
+
+        h[h_index, 2] = code_index
+
+    return h
+
+# ------------------ ENCODE ------------------
+
+def encode_huff(coeff_sec, huff_LUT_list, force_codebook = None):
+    """
+    Huffman-encode a sequence of quantized coefficients.
+
+    This function selects the appropriate Huffman codebook based on the
+    maximum absolute value of the input coefficients, encodes the coefficients
+    into a binary Huffman bitstream, and returns both the bitstream and the
+    selected codebook index.
+
+    This is the Python equivalent of the MATLAB `encodeHuff.m` function used
+    in audio/image coding (e.g., scale factor band encoding). The input
+    coefficient sequence is grouped into fixed-size tuples as defined by
+    the chosen Huffman LUT. Zero-padding may be applied internally.
+
+    Parameters
+    ----------
+    coeff_sec : array_like of int
+        1-D array of quantized integer coefficients to encode.
+        Typically corresponds to a "section" or scale-factor band.
+
+    huff_LUT_list : list
+        List of Huffman lookup-table dictionaries as returned by `loadLUT()`.
+        Index 1..11 correspond to valid Huffman codebooks.
+        Index 0 is unused.
+
+    Returns
+    -------
+    huffSec : str
+        Huffman-encoded bitstream represented as a string of '0' and '1'
+        characters.
+
+    huffCodebook : int
+        Index (1..11) of the Huffman codebook used for encoding.
+        A value of 0 indicates a special all-zero section.
+    """
+    if force_codebook is not None:
+            return huff_LUT_code_1(huff_LUT_list[force_codebook], coeff_sec)
+    
+    maxAbsVal = np.max(np.abs(coeff_sec))
+
+    if maxAbsVal == 0:
+        huffCodebook = 0
+        huffSec = huff_LUT_code_0()
+
+    elif maxAbsVal == 1:
+        candidates = [1, 2]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal == 2:
+        candidates = [3, 4]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal in (3, 4):
+        candidates = [5, 6]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal in (5, 6, 7):
+        candidates = [7, 8]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal in (8, 9, 10, 11, 12):
+        candidates = [9, 10]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal in (13, 14, 15):
+        huffCodebook = 11
+        huffSec = huff_LUT_code_1(huff_LUT_list[huffCodebook], coeff_sec)
+
+    else:
+        huffCodebook = 11
+        huffSec = huff_LUT_code_ESC(huff_LUT_list[huffCodebook], coeff_sec)
+
+    return huffSec, huffCodebook
+
+def huff_LUT_code_1(huff_LUT, coeff_sec):
+    LUT = huff_LUT['LUT']
+    nTupleSize = huff_LUT['nTupleSize']
+    maxAbsCodeVal = huff_LUT['maxAbsCodeVal']
+    signedValues = huff_LUT['signedValues']
+
+    numTuples = int(np.ceil(len(coeff_sec) / nTupleSize))
+
+    if signedValues:
+        coeff = coeff_sec + maxAbsCodeVal
+        base = 2 * maxAbsCodeVal + 1
+    else:
+        coeff = coeff_sec
+        base = maxAbsCodeVal + 1
+
+    coeffPad = np.zeros(numTuples * nTupleSize, dtype=int)
+    coeffPad[:len(coeff)] = coeff
+
+    huffSec = []
+
+    powers = base ** np.arange(nTupleSize - 1, -1, -1)
+
+    for i in range(numTuples):
+        nTuple = coeffPad[i*nTupleSize:(i+1)*nTupleSize]
+        huffIndex = int(np.abs(nTuple) @ powers)
+
+        hexVal = LUT[huffIndex, 2]
+        huffLen = LUT[huffIndex, 1]
+
+        bits = format(int(hexVal), f'0{int(huffLen)}b')
+
+        if signedValues:
+            huffSec.append(bits)
+        else:
+            signBits = ''.join('1' if v < 0 else '0' for v in nTuple)
+            huffSec.append(bits + signBits)
+
+    return ''.join(huffSec)
+
+def huff_LUT_code_0():
+    return ''
+
+def huff_LUT_code_ESC(huff_LUT, coeff_sec):
+    LUT = huff_LUT['LUT']
+    nTupleSize = huff_LUT['nTupleSize']
+    maxAbsCodeVal = huff_LUT['maxAbsCodeVal']
+
+    numTuples = int(np.ceil(len(coeff_sec) / nTupleSize))
+    base = maxAbsCodeVal + 1
+
+    coeffPad = np.zeros(numTuples * nTupleSize, dtype=int)
+    coeffPad[:len(coeff_sec)] = coeff_sec
+
+    huffSec = []
+    powers = base ** np.arange(nTupleSize - 1, -1, -1)
+
+    for i in range(numTuples):
+        nTuple = coeffPad[i*nTupleSize:(i+1)*nTupleSize]
+
+        lnTuple = nTuple.astype(float)
+        lnTuple[lnTuple == 0] = np.finfo(float).eps
+
+        N4 = np.maximum(0, np.floor(np.log2(np.abs(lnTuple))).astype(int))
+        N = np.maximum(0, N4 - 4)
+        esc = np.abs(nTuple) > 15
+
+        nTupleESC = nTuple.copy()
+        nTupleESC[esc] = np.sign(nTupleESC[esc]) * 16
+
+        huffIndex = int(np.abs(nTupleESC) @ powers)
+
+        hexVal = LUT[huffIndex, 2]
+        huffLen = LUT[huffIndex, 1]
+
+        bits = format(int(hexVal), f'0{int(huffLen)}b')
+
+        escSeq = ''
+        for k in range(nTupleSize):
+            if esc[k]:
+                escSeq += '1' * N[k]
+                escSeq += '0'
+                escSeq += format(abs(nTuple[k]) - (1 << N4[k]), f'0{N4[k]}b')
+
+        signBits = ''.join('1' if v < 0 else '0' for v in nTuple)
+        huffSec.append(bits + signBits + escSeq)
+
+    return ''.join(huffSec)
+
+# ------------------ DECODE ------------------
+
+def decode_huff(huff_sec, huff_LUT):
+    """
+    Decode a Huffman-encoded stream.
+
+    Parameters
+    ----------
+    huff_sec : array-like of int or str
+        Huffman encoded stream as a sequence of 0 and 1 (string or list/array).
+    huff_LUT : dict
+        Huffman lookup table with keys:
+            - 'invTable': inverse table (numpy array)
+            - 'codebook': codebook number
+            - 'nTupleSize': tuple size
+            - 'maxAbsCodeVal': maximum absolute code value
+            - 'signedValues': True/False
+
+    Returns
+    -------
+    decCoeffs : list of int
+        Decoded quantized coefficients.
+    """
+
+    h = huff_LUT['invTable']
+    huffCodebook = huff_LUT['codebook']
+    nTupleSize = huff_LUT['nTupleSize']
+    maxAbsCodeVal = huff_LUT['maxAbsCodeVal']
+    signedValues = huff_LUT['signedValues']
+
+    # Convert string to array of ints
+    if isinstance(huff_sec, str):
+        huff_sec = np.array([int(b) for b in huff_sec])
+
+    eos = False
+    decCoeffs = []
+    streamIndex = 0
+
+    while not eos:
+        wordbit = 0
+        r = 0  # start at root
+
+        # Decode Huffman word using inverse table
+        while True:
+            b = huff_sec[streamIndex + wordbit]
+            wordbit += 1
+            rOld = r
+            r = h[rOld, b]
+            if h[r, 0] == 0 and h[r, 1] == 0:
+                symbolIndex = h[r, 2] - 1  # zero-based
+                streamIndex += wordbit
+                break
+
+        # Decode n-tuple magnitudes
+        if signedValues:
+            base = 2 * maxAbsCodeVal + 1
+            nTupleDec = []
+            tmp = symbolIndex
+            for p in reversed(range(nTupleSize)):
+                val = tmp // (base ** p)
+                nTupleDec.append(val - maxAbsCodeVal)
+                tmp = tmp % (base ** p)
+            nTupleDec = np.array(nTupleDec)
+        else:
+            base = maxAbsCodeVal + 1
+            nTupleDec = []
+            tmp = symbolIndex
+            for p in reversed(range(nTupleSize)):
+                val = tmp // (base ** p)
+                nTupleDec.append(val)
+                tmp = tmp % (base ** p)
+            nTupleDec = np.array(nTupleDec)
+
+            # Apply sign bits
+            nTupleSignBits = huff_sec[streamIndex:streamIndex + nTupleSize]
+            nTupleSign = -(np.sign(nTupleSignBits - 0.5))
+            streamIndex += nTupleSize
+            nTupleDec = nTupleDec * nTupleSign
+
+        # Handle escape sequences
+        escIndex = np.where(np.abs(nTupleDec) == 16)[0]
+        if huffCodebook == 11 and escIndex.size > 0:
+            for idx in escIndex:
+                N = 0
+                b = huff_sec[streamIndex]
+                while b:
+                    N += 1
+                    b = huff_sec[streamIndex + N]
+                streamIndex += N
+                N4 = N + 4
+                escape_word = huff_sec[streamIndex:streamIndex + N4]
+                escape_value = 2 ** N4 + int("".join(map(str, escape_word)), 2)
+                nTupleDec[idx] = escape_value
+                streamIndex += N4 + 1
+            # Apply signs again
+            nTupleDec[escIndex] *= nTupleSign[escIndex]
+
+        decCoeffs.extend(nTupleDec.tolist())
+
+        if streamIndex >= len(huff_sec):
+            eos = True
+
+    return decCoeffs
+    
+

source/level_2/core/aac_coder.py

@@ -0,0 +1,284 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - AAC Coder (Core)
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   Level 1 AAC encoder orchestration.
+#   Keeps the same functional behavior as the original level_1 implementation:
+#   - Reads WAV via soundfile
+#   - Validates stereo and 48 kHz
+#   - Frames into 2048 samples with hop=1024 and zero padding at both ends
+#   - SSC decision uses next-frame attack detection
+#   - Filterbank analysis (MDCT)
+#   - Stores per-channel spectra in AACSeq1 schema:
+#       * ESH: (128, 8)
+#       * else: (1024, 1)
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Union
+
+import soundfile as sf
+
+from core.aac_configuration import WIN_TYPE
+from core.aac_filterbank import aac_filter_bank
+from core.aac_ssc import aac_SSC
+from core.aac_tns import aac_tns
+from core.aac_types import *
+
+
+# -----------------------------------------------------------------------------
+# Public helpers (useful for level_x demo wrappers)
+# -----------------------------------------------------------------------------
+
+def aac_read_wav_stereo_48k(filename_in: Union[str, Path]) -> tuple[StereoSignal, int]:
+    """
+    Read a WAV file using soundfile and validate the Level-1 assumptions.
+
+    Parameters
+    ----------
+    filename_in : Union[str, Path]
+        Input WAV filename.
+
+    Returns
+    -------
+    x : StereoSignal (np.ndarray)
+        Stereo samples as float64, shape (N, 2).
+    fs : int
+        Sampling rate (Hz). Must be 48000.
+
+    Raises
+    ------
+    ValueError
+        If the input is not stereo or the sampling rate is not 48 kHz.
+    """
+    filename_in = Path(filename_in)
+
+    x, fs = sf.read(str(filename_in), always_2d=True)
+    x = np.asarray(x, dtype=np.float64)
+
+    if x.shape[1] != 2:
+        raise ValueError("Input must be stereo (2 channels).")
+    if int(fs) != 48000:
+        raise ValueError("Input sampling rate must be 48 kHz.")
+
+    return x, int(fs)
+
+
+def aac_pack_frame_f_to_seq_channels(frame_type: FrameType, frame_f: FrameF) -> tuple[FrameChannelF, FrameChannelF]:
+    """
+    Convert the stereo FrameF returned by aac_filter_bank() into per-channel arrays
+    as required by the Level-1 AACSeq1 schema.
+
+    Parameters
+    ----------
+    frame_type : FrameType
+        "OLS" | "LSS" | "ESH" | "LPS".
+    frame_f : FrameF
+        Output of aac_filter_bank():
+        - If frame_type != "ESH": shape (1024, 2)
+        - If frame_type == "ESH": shape (128, 16) packed as [L0 R0 L1 R1 ... L7 R7]
+
+    Returns
+    -------
+    chl_f : FrameChannelF
+        Left channel coefficients:
+        - ESH: shape (128, 8)
+        - else: shape (1024, 1)
+    chr_f : FrameChannelF
+        Right channel coefficients:
+        - ESH: shape (128, 8)
+        - else: shape (1024, 1)
+    """
+    if frame_type == "ESH":
+        if frame_f.shape != (128, 16):
+            raise ValueError("For ESH, frame_f must have shape (128, 16).")
+
+        chl_f = np.empty((128, 8), dtype=np.float64)
+        chr_f = np.empty((128, 8), dtype=np.float64)
+        for j in range(8):
+            chl_f[:, j] = frame_f[:, 2 * j + 0]
+            chr_f[:, j] = frame_f[:, 2 * j + 1]
+        return chl_f, chr_f
+
+    # Non-ESH: store as (1024, 1) as required by the original Level-1 schema.
+    if frame_f.shape != (1024, 2):
+        raise ValueError("For OLS/LSS/LPS, frame_f must have shape (1024, 2).")
+
+    chl_f = frame_f[:, 0:1].astype(np.float64, copy=False)
+    chr_f = frame_f[:, 1:2].astype(np.float64, copy=False)
+    return chl_f, chr_f
+
+
+
+# -----------------------------------------------------------------------------
+# Level 1 encoder
+# -----------------------------------------------------------------------------
+
+def aac_coder_1(filename_in: Union[str, Path]) -> AACSeq1:
+    """
+    Level-1 AAC encoder.
+
+    This function preserves the behavior of the original level_1 implementation:
+    - Read stereo 48 kHz WAV
+    - Pad hop samples at start and hop samples at end
+    - Frame with win=2048, hop=1024
+    - Use SSC with next-frame lookahead
+    - Apply filterbank analysis
+    - Store per-channel coefficients using AACSeq1 schema
+
+    Parameters
+    ----------
+    filename_in : Union[str, Path]
+        Input WAV filename.
+        Assumption: stereo audio, sampling rate 48 kHz.
+
+    Returns
+    -------
+    AACSeq1
+        List of encoded frames (Level 1 schema).
+    """
+    x, _ = aac_read_wav_stereo_48k(filename_in)
+    # The assignment assumes 48 kHz
+
+    hop = 1024
+    win = 2048
+
+    # Pad at the beginning to support the first overlap region.
+    # Tail padding is kept minimal; next-frame is padded on-the-fly when needed.
+    pad_pre = np.zeros((hop, 2), dtype=np.float64)
+    pad_post = np.zeros((hop, 2), dtype=np.float64)
+    x_pad = np.vstack([pad_pre, x, pad_post])
+
+    # Number of frames such that current frame fits; next frame will be padded if needed.
+    K = int((x_pad.shape[0] - win) // hop + 1)
+    if K <= 0:
+        raise ValueError("Input too short for framing.")
+
+    aac_seq: AACSeq1 = []
+    prev_frame_type: FrameType = "OLS"
+
+    win_type: WinType = WIN_TYPE
+
+    for i in range(K):
+        start = i * hop
+
+        frame_t: FrameT = x_pad[start:start + win, :]
+        if frame_t.shape != (win, 2):
+            # This should not happen due to K definition, but keep it explicit.
+            raise ValueError("Internal framing error: frame_t has wrong shape.")
+
+        next_t = x_pad[start + hop:start + hop + win, :]
+
+        # Ensure next_t is always (2048, 2) by zero-padding at the tail.
+        if next_t.shape[0] < win:
+            tail = np.zeros((win - next_t.shape[0], 2), dtype=np.float64)
+            next_t = np.vstack([next_t, tail])
+
+        frame_type = aac_SSC(frame_t, next_t, prev_frame_type)
+        frame_f = aac_filter_bank(frame_t, frame_type, win_type)
+
+        chl_f, chr_f = aac_pack_frame_f_to_seq_channels(frame_type, frame_f)
+
+        aac_seq.append({
+            "frame_type": frame_type,
+            "win_type": win_type,
+            "chl": {"frame_F": chl_f},
+            "chr": {"frame_F": chr_f},
+        })
+
+        prev_frame_type = frame_type
+
+    return aac_seq
+
+
+def aac_coder_2(filename_in: Union[str, Path]) -> AACSeq2:
+    """
+    Level-2 AAC encoder (Level 1 + TNS).
+
+    Parameters
+    ----------
+    filename_in : Union[str, Path]
+        Input WAV filename (stereo, 48 kHz).
+
+    Returns
+    -------
+    AACSeq2
+        Encoded AAC sequence (Level 2 payload schema).
+        For each frame i:
+        - "frame_type": FrameType
+        - "win_type": WinType
+        - "chl"/"chr":
+            - "frame_F": FrameChannelF (after TNS)
+            - "tns_coeffs": TnsCoeffs
+    """
+    filename_in = Path(filename_in)
+
+    x, _ = aac_read_wav_stereo_48k(filename_in)
+    # The assignment assumes 48 kHz
+
+    hop = 1024
+    win = 2048
+
+    pad_pre = np.zeros((hop, 2), dtype=np.float64)
+    pad_post = np.zeros((hop, 2), dtype=np.float64)
+    x_pad = np.vstack([pad_pre, x, pad_post])
+
+    K = int((x_pad.shape[0] - win) // hop + 1)
+    if K <= 0:
+        raise ValueError("Input too short for framing.")
+
+    aac_seq: AACSeq2 = []
+    prev_frame_type: FrameType = "OLS"
+
+    for i in range(K):
+        start = i * hop
+
+        frame_t: FrameT = x_pad[start : start + win, :]
+        if frame_t.shape != (win, 2):
+            raise ValueError("Internal framing error: frame_t has wrong shape.")
+
+        next_t = x_pad[start + hop : start + hop + win, :]
+        if next_t.shape[0] < win:
+            tail = np.zeros((win - next_t.shape[0], 2), dtype=np.float64)
+            next_t = np.vstack([next_t, tail])
+
+        frame_type = aac_SSC(frame_t, next_t, prev_frame_type)
+
+        # Level 1 analysis (packed stereo container)
+        frame_f_stereo = aac_filter_bank(frame_t, frame_type, WIN_TYPE)
+
+        # Unpack to per-channel (as you already do in Level 1)
+        if frame_type == "ESH":
+            chl_f = np.empty((128, 8), dtype=np.float64)
+            chr_f = np.empty((128, 8), dtype=np.float64)
+            for j in range(8):
+                chl_f[:, j] = frame_f_stereo[:, 2 * j + 0]
+                chr_f[:, j] = frame_f_stereo[:, 2 * j + 1]
+        else:
+            chl_f = frame_f_stereo[:, 0:1].astype(np.float64, copy=False)
+            chr_f = frame_f_stereo[:, 1:2].astype(np.float64, copy=False)
+
+        # Level 2: apply TNS per channel
+        chl_f_tns, chl_tns_coeffs = aac_tns(chl_f, frame_type)
+        chr_f_tns, chr_tns_coeffs = aac_tns(chr_f, frame_type)
+
+        aac_seq.append(
+            {
+                "frame_type": frame_type,
+                "win_type": WIN_TYPE,
+                "chl": {"frame_F": chl_f_tns, "tns_coeffs": chl_tns_coeffs},
+                "chr": {"frame_F": chr_f_tns, "tns_coeffs": chr_tns_coeffs},
+            }
+        )
+
+        prev_frame_type = frame_type
+
+    return aac_seq

source/level_2/core/aac_configuration.py

@@ -0,0 +1,31 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - Configuration
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   This module contains the global configurations
+#
+# ------------------------------------------------------------
+from __future__ import annotations
+
+# Imports
+from core.aac_types import WinType
+
+# Filterbank
+# ------------------------------------------------------------
+# Window type
+# Options: "SIN", "KBD"
+WIN_TYPE: WinType = "SIN"
+
+
+# TNS
+# ------------------------------------------------------------
+PRED_ORDER = 4
+QUANT_STEP = 0.1
+QUANT_MAX = 0.7  # 4-bit symmetric with step 0.1 -> clamp to [-0.7, +0.7]

source/level_2/core/aac_decoder.py

@@ -0,0 +1,257 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - Inverse AAC Coder (Core)
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   Level 1 AAC decoder orchestration (inverse of aac_coder_1()).
+#   Keeps the same functional behavior as the original level_1 implementation:
+#   - Re-pack per-channel spectra into FrameF expected by aac_i_filter_bank()
+#   - IMDCT synthesis per frame
+#   - Overlap-add with hop=1024
+#   - Remove encoder boundary padding: hop at start and hop at end
+#
+#   Note:
+#   This core module returns the reconstructed samples. Writing to disk is kept
+#   in level_x demos.
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Union
+
+import soundfile as sf
+
+from core.aac_filterbank import aac_i_filter_bank
+from core.aac_tns import aac_i_tns
+from core.aac_types import *
+
+
+# -----------------------------------------------------------------------------
+# Public helpers (useful for level_x demo wrappers)
+# -----------------------------------------------------------------------------
+
+def aac_unpack_seq_channels_to_frame_f(frame_type: FrameType, chl_f: FrameChannelF, chr_f: FrameChannelF) -> FrameF:
+    """
+    Re-pack per-channel spectra from the Level-1 AACSeq1 schema into the stereo
+    FrameF container expected by aac_i_filter_bank().
+
+    Parameters
+    ----------
+    frame_type : FrameType
+        "OLS" | "LSS" | "ESH" | "LPS".
+    chl_f : FrameChannelF
+        Left channel coefficients:
+        - ESH: (128, 8)
+        - else: (1024, 1)
+    chr_f : FrameChannelF
+        Right channel coefficients:
+        - ESH: (128, 8)
+        - else: (1024, 1)
+
+    Returns
+    -------
+    FrameF
+        Stereo coefficients:
+        - ESH: (128, 16) packed as [L0 R0 L1 R1 ... L7 R7]
+        - else: (1024, 2)
+    """
+    if frame_type == "ESH":
+        if chl_f.shape != (128, 8) or chr_f.shape != (128, 8):
+            raise ValueError("ESH channel frame_F must have shape (128, 8).")
+
+        frame_f = np.empty((128, 16), dtype=np.float64)
+        for j in range(8):
+            frame_f[:, 2 * j + 0] = chl_f[:, j]
+            frame_f[:, 2 * j + 1] = chr_f[:, j]
+        return frame_f
+
+    # Non-ESH: expected (1024, 1) per channel in Level-1 schema.
+    if chl_f.shape != (1024, 1) or chr_f.shape != (1024, 1):
+        raise ValueError("Non-ESH channel frame_F must have shape (1024, 1).")
+
+    frame_f = np.empty((1024, 2), dtype=np.float64)
+    frame_f[:, 0] = chl_f[:, 0]
+    frame_f[:, 1] = chr_f[:, 0]
+    return frame_f
+
+
+def aac_remove_padding(y_pad: StereoSignal, hop: int = 1024) -> StereoSignal:
+    """
+    Remove the boundary padding that the Level-1 encoder adds:
+    hop samples at start and hop samples at end.
+
+    Parameters
+    ----------
+    y_pad : StereoSignal (np.ndarray)
+        Reconstructed padded stream, shape (N_pad, 2).
+    hop : int
+        Hop size in samples (default 1024).
+
+    Returns
+    -------
+    StereoSignal (np.ndarray)
+        Unpadded reconstructed stream, shape (N_pad - 2*hop, 2).
+
+    Raises
+    ------
+    ValueError
+        If y_pad is too short to unpad.
+    """
+    if y_pad.shape[0] < 2 * hop:
+        raise ValueError("Decoded stream too short to unpad.")
+    return y_pad[hop:-hop, :]
+
+
+# -----------------------------------------------------------------------------
+# Level 1 decoder (core)
+# -----------------------------------------------------------------------------
+
+def aac_decoder_1(aac_seq_1: AACSeq1, filename_out: Union[str, Path]) -> StereoSignal:
+    """
+    Level-1 AAC decoder (inverse of aac_coder_1()).
+
+    This function preserves the behavior of the original level_1 implementation:
+    - Reconstruct the full padded stream by overlap-adding K synthesized frames
+    - Remove hop padding at the beginning and hop padding at the end
+    - Write the reconstructed stereo WAV file (48 kHz)
+    - Return reconstructed stereo samples as float64
+
+    Parameters
+    ----------
+    aac_seq_1 : AACSeq1
+        Encoded sequence as produced by aac_coder_1().
+    filename_out : Union[str, Path]
+        Output WAV filename. Assumption: 48 kHz, stereo.
+
+    Returns
+    -------
+    StereoSignal
+        Decoded audio samples (time-domain), stereo, shape (N, 2), dtype float64.
+    """
+    filename_out = Path(filename_out)
+
+    hop = 1024
+    win = 2048
+    K = len(aac_seq_1)
+
+    # Output includes the encoder padding region, so we reconstruct the full padded stream.
+    # For K frames: last frame starts at (K-1)*hop and spans win,
+    # so total length = (K-1)*hop + win.
+    n_pad = (K - 1) * hop + win
+    y_pad: StereoSignal = np.zeros((n_pad, 2), dtype=np.float64)
+
+    for i, fr in enumerate(aac_seq_1):
+        frame_type: FrameType = fr["frame_type"]
+        win_type: WinType = fr["win_type"]
+
+        chl_f = np.asarray(fr["chl"]["frame_F"], dtype=np.float64)
+        chr_f = np.asarray(fr["chr"]["frame_F"], dtype=np.float64)
+
+        frame_f: FrameF = aac_unpack_seq_channels_to_frame_f(frame_type, chl_f, chr_f)
+        frame_t_hat: FrameT = aac_i_filter_bank(frame_f, frame_type, win_type)  # (2048, 2)
+
+        start = i * hop
+        y_pad[start:start + win, :] += frame_t_hat
+
+    y: StereoSignal = aac_remove_padding(y_pad, hop=hop)
+
+    # Level 1 assumption: 48 kHz output.
+    sf.write(str(filename_out), y, 48000)
+
+    return y
+
+
+def aac_decoder_2(aac_seq_2: AACSeq2, filename_out: Union[str, Path]) -> StereoSignal:
+    """
+    Level-2 AAC decoder (inverse of aac_coder_2).
+
+    Behavior matches Level 1 decoder pipeline, with additional iTNS stage:
+    - Per frame/channel: inverse TNS using stored coefficients
+    - Re-pack to stereo frame_F
+    - IMDCT + windowing
+    - Overlap-add over frames
+    - Remove Level-1 padding (hop samples start/end)
+    - Write output WAV (48 kHz)
+
+    Parameters
+    ----------
+    aac_seq_2 : AACSeq2
+        Encoded sequence as produced by aac_coder_2().
+    filename_out : Union[str, Path]
+        Output WAV filename.
+
+    Returns
+    -------
+    StereoSignal
+        Decoded audio samples (time-domain), stereo, shape (N, 2), dtype float64.
+    """
+    filename_out = Path(filename_out)
+
+    hop = 1024
+    win = 2048
+    K = len(aac_seq_2)
+
+    if K <= 0:
+        raise ValueError("aac_seq_2 must contain at least one frame.")
+
+    n_pad = (K - 1) * hop + win
+    y_pad = np.zeros((n_pad, 2), dtype=np.float64)
+
+    for i, fr in enumerate(aac_seq_2):
+        frame_type: FrameType = fr["frame_type"]
+        win_type: WinType = fr["win_type"]
+
+        chl_f_tns = np.asarray(fr["chl"]["frame_F"], dtype=np.float64)
+        chr_f_tns = np.asarray(fr["chr"]["frame_F"], dtype=np.float64)
+
+        chl_coeffs = np.asarray(fr["chl"]["tns_coeffs"], dtype=np.float64)
+        chr_coeffs = np.asarray(fr["chr"]["tns_coeffs"], dtype=np.float64)
+
+        # Inverse TNS per channel
+        chl_f = aac_i_tns(chl_f_tns, frame_type, chl_coeffs)
+        chr_f = aac_i_tns(chr_f_tns, frame_type, chr_coeffs)
+
+        # Re-pack to the stereo container expected by aac_i_filter_bank
+        if frame_type == "ESH":
+            if chl_f.shape != (128, 8) or chr_f.shape != (128, 8):
+                raise ValueError("ESH channel frame_F must have shape (128, 8).")
+
+            frame_f: FrameF = np.empty((128, 16), dtype=np.float64)
+            for j in range(8):
+                frame_f[:, 2 * j + 0] = chl_f[:, j]
+                frame_f[:, 2 * j + 1] = chr_f[:, j]
+        else:
+            # Accept either (1024,1) or (1024,) from your internal convention.
+            if chl_f.shape == (1024,):
+                chl_col = chl_f.reshape(1024, 1)
+            elif chl_f.shape == (1024, 1):
+                chl_col = chl_f
+            else:
+                raise ValueError("Non-ESH left channel frame_F must be shape (1024,) or (1024, 1).")
+
+            if chr_f.shape == (1024,):
+                chr_col = chr_f.reshape(1024, 1)
+            elif chr_f.shape == (1024, 1):
+                chr_col = chr_f
+            else:
+                raise ValueError("Non-ESH right channel frame_F must be shape (1024,) or (1024, 1).")
+
+            frame_f = np.empty((1024, 2), dtype=np.float64)
+            frame_f[:, 0] = chl_col[:, 0]
+            frame_f[:, 1] = chr_col[:, 0]
+
+        frame_t_hat: FrameT = aac_i_filter_bank(frame_f, frame_type, win_type)
+
+        start = i * hop
+        y_pad[start : start + win, :] += frame_t_hat
+
+    y = aac_remove_padding(y_pad, hop=hop)
+
+    sf.write(str(filename_out), y, 48000)
+    return y

source/level_2/core/aac_filterbank.py

@@ -0,0 +1,454 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - Filterbank module
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   Filterbank stage (MDCT/IMDCT), windowing, ESH packing/unpacking
+#
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from core.aac_types import *
+
+from scipy.signal.windows import kaiser
+
+# Private helpers for Filterbank
+# ------------------------------------------------------------
+
+def _sin_window(N: int) -> Window:
+    """
+    Build a sinusoidal (SIN) window of length N.
+
+    The AAC sinusoid window is:
+        w[n] = sin(pi/N * (n + 0.5)),  for 0 <= n < N
+
+    Parameters
+    ----------
+    N : int
+        Window length in samples.
+
+    Returns
+    -------
+    Window
+        1-D array of shape (N, ) with dtype float64.
+    """
+    n = np.arange(N, dtype=np.float64)
+    return np.sin((np.pi / N) * (n + 0.5))
+
+
+def _kbd_window(N: int, alpha: float) -> Window:
+    """
+    Build a Kaiser-Bessel-Derived (KBD) window of length N.
+
+    This follows the standard KBD construction used in AAC:
+      1) Build a Kaiser kernel of length (N/2 + 1).
+      2) Form the left half by cumulative summation, normalization, and sqrt.
+      3) Mirror the left half to form the right half (symmetric full-length window).
+
+    Notes
+    -----
+    - N must be even (AAC uses N=2048 for long and N=256 for short).
+    - The assignment specifies alpha=6 for long windows and alpha=4 for short windows.
+    - The Kaiser beta parameter is commonly taken as beta = pi * alpha for this context.
+
+    Parameters
+    ----------
+    N : int
+        Window length in samples (must be even).
+    alpha : float
+        KBD alpha parameter.
+
+    Returns
+    -------
+    Window
+        1-D array of shape (N,) with dtype float64.
+    """
+    half = N // 2
+
+    # Kaiser kernel length: half + 1 samples (0 .. half)
+    # beta = pi * alpha per the usual correspondence with the ISO definition
+    kernel = kaiser(half + 1, beta=np.pi * alpha).astype(np.float64)
+
+    csum = np.cumsum(kernel)
+    denom = csum[-1]
+
+    w_left = np.sqrt(csum[:-1] / denom)  # length half, n = 0 .. half-1
+    w_right = w_left[::-1]               # mirror for second half
+
+    return np.concatenate([w_left, w_right])
+
+
+def _long_window(win_type: WinType) -> Window:
+    """
+    Return the long AAC window (length 2048) for the selected window family.
+
+    Parameters
+    ----------
+    win_type : WinType
+        Either "SIN" or "KBD".
+
+    Returns
+    -------
+    Window
+        1-D array of shape (2048,) with dtype float64.
+    """
+    if win_type == "SIN":
+        return _sin_window(2048)
+    if win_type == "KBD":
+        # Assignment-specific alpha values
+        return _kbd_window(2048, alpha=6.0)
+    raise ValueError(f"Invalid win_type: {win_type!r}")
+
+
+def _short_window(win_type: WinType) -> Window:
+    """
+    Return the short AAC window (length 256) for the selected window family.
+
+    Parameters
+    ----------
+    win_type : WinType
+        Either "SIN" or "KBD".
+
+    Returns
+    -------
+    Window
+        1-D array of shape (256,) with dtype float64.
+    """
+    if win_type == "SIN":
+        return _sin_window(256)
+    if win_type == "KBD":
+        # Assignment-specific alpha values
+        return _kbd_window(256, alpha=4.0)
+    raise ValueError(f"Invalid win_type: {win_type!r}")
+
+
+def _window_sequence(frame_type: FrameType, win_type: WinType) -> Window:
+    """
+    Build the 2048-sample analysis/synthesis window for OLS/LSS/LPS.
+
+    In this assignment we assume a single window family is used globally
+    (no mixed KBD/SIN halves). Therefore, both the long and short windows
+    are drawn from the same family.
+
+    For frame_type:
+    - "OLS": return the long window Wl (2048).
+    - "LSS": construct [Wl_left(1024), ones(448), Ws_right(128), zeros(448)].
+    - "LPS": construct [zeros(448), Ws_left(128), ones(448), Wl_right(1024)].
+
+    Parameters
+    ----------
+    frame_type : FrameType
+        One of "OLS", "LSS", "LPS".
+    win_type : WinType
+        Either "SIN" or "KBD".
+
+    Returns
+    -------
+    Window
+        1-D array of shape (2048,) with dtype float64.
+    """
+    wL = _long_window(win_type)   # length 2048
+    wS = _short_window(win_type)  # length 256
+
+    if frame_type == "OLS":
+        return wL
+
+    if frame_type == "LSS":
+        # 0..1023: left half of long window
+        # 1024..1471: ones (448 samples)
+        # 1472..1599: right half of short window (128 samples)
+        # 1600..2047: zeros (448 samples)
+        out = np.zeros(2048, dtype=np.float64)
+        out[0:1024] = wL[0:1024]
+        out[1024:1472] = 1.0
+        out[1472:1600] = wS[128:256]
+        out[1600:2048] = 0.0
+        return out
+
+    if frame_type == "LPS":
+        # 0..447: zeros (448)
+        # 448..575: left half of short window (128)
+        # 576..1023: ones (448)
+        # 1024..2047: right half of long window (1024)
+        out = np.zeros(2048, dtype=np.float64)
+        out[0:448] = 0.0
+        out[448:576] = wS[0:128]
+        out[576:1024] = 1.0
+        out[1024:2048] = wL[1024:2048]
+        return out
+
+    raise ValueError(f"Invalid frame_type for long window sequence: {frame_type!r}")
+
+
+def _mdct(s: TimeSignal) -> MdctCoeffs:
+    """
+    MDCT (direct form) as specified in the assignment.
+
+    Parameters
+    ----------
+    s : TimeSignal
+        Windowed time samples, 1-D array of length N (N = 2048 or 256).
+
+    Returns
+    -------
+    MdctCoeffs
+        MDCT coefficients, 1-D array of length N/2.
+
+    Definition
+    ----------
+    X[k] = 2 * sum_{n=0..N-1} s[n] * cos((2*pi/N) * (n + n0) * (k + 1/2)),
+    where n0 = (N/2 + 1)/2.
+    """
+    s = np.asarray(s, dtype=np.float64).reshape(-1)
+    N = int(s.shape[0])
+    if N not in (2048, 256):
+        raise ValueError("MDCT input length must be 2048 or 256.")
+
+    n0 = (N / 2.0 + 1.0) / 2.0
+    n = np.arange(N, dtype=np.float64) + n0
+    k = np.arange(N // 2, dtype=np.float64) + 0.5
+
+    C = np.cos((2.0 * np.pi / N) * np.outer(n, k))  # (N, N/2)
+    X = 2.0 * (s @ C)  # (N/2,)
+    return X
+
+
+def _imdct(X: MdctCoeffs) -> TimeSignal:
+    """
+    IMDCT (direct form) as specified in the assignment.
+
+    Parameters
+    ----------
+    X : MdctCoeffs
+        MDCT coefficients, 1-D array of length K (K = 1024 or 128).
+
+    Returns
+    -------
+    TimeSignal
+        Reconstructed time samples, 1-D array of length N = 2K.
+
+    Definition
+    ----------
+    s[n] = (2/N) * sum_{k=0..N/2-1} X[k] * cos((2*pi/N) * (n + n0) * (k + 1/2)),
+    where n0 = (N/2 + 1)/2.
+    """
+    X = np.asarray(X, dtype=np.float64).reshape(-1)
+    K = int(X.shape[0])
+    if K not in (1024, 128):
+        raise ValueError("IMDCT input length must be 1024 or 128.")
+
+    N = 2 * K
+    n0 = (N / 2.0 + 1.0) / 2.0
+
+    n = np.arange(N, dtype=np.float64) + n0
+    k = np.arange(K, dtype=np.float64) + 0.5
+
+    C = np.cos((2.0 * np.pi / N) * np.outer(n, k))  # (N, K)
+    s = (2.0 / N) * (C @ X)  # (N,)
+    return s
+
+
+def _filter_bank_esh_channel(x_ch: FrameChannelT, win_type: WinType) -> FrameChannelF:
+    """
+    ESH analysis for one channel.
+
+    Parameters
+    ----------
+    x_ch : FrameChannelT
+        Time-domain channel frame (expected shape: (2048,)).
+    win_type : WinType
+        Window family ("KBD" or "SIN").
+
+    Returns
+    -------
+    FrameChannelF
+        Array of shape (128, 8). Column j contains the 128 MDCT coefficients
+        of the j-th short window.
+    """
+    wS = _short_window(win_type)  # (256,)
+    X_esh = np.empty((128, 8), dtype=np.float64)
+
+    # ESH subwindows are taken from the central region:
+    # start positions: 448 + 128*j, j = 0..7
+    for j in range(8):
+        start = 448 + 128 * j
+        seg = x_ch[start:start + 256] * wS  # (256,)
+        X_esh[:, j] = _mdct(seg)           # (128,)
+
+    return X_esh
+
+
+def _unpack_esh(frame_F: FrameF) -> tuple[FrameChannelF, FrameChannelF]:
+    """
+    Unpack ESH spectrum from shape (128, 16) into per-channel arrays (128, 8).
+
+    Parameters
+    ----------
+    frame_F : FrameF
+        Packed ESH spectrum (expected shape: (128, 16)).
+
+    Returns
+    -------
+    left : FrameChannelF
+        Left channel spectrum, shape (128, 8).
+    right : FrameChannelF
+        Right channel spectrum, shape (128, 8).
+
+    Notes
+    -----
+    Inverse mapping of the packing used in aac_filter_bank():
+      packed[:, 2*j]   = left[:, j]
+      packed[:, 2*j+1] = right[:, j]
+    """
+    if frame_F.shape != (128, 16):
+        raise ValueError("ESH frame_F must have shape (128, 16).")
+
+    left = np.empty((128, 8), dtype=np.float64)
+    right = np.empty((128, 8), dtype=np.float64)
+    for j in range(8):
+        left[:, j] = frame_F[:, 2 * j + 0]
+        right[:, j] = frame_F[:, 2 * j + 1]
+    return left, right
+
+
+def _i_filter_bank_esh_channel(X_esh: FrameChannelF, win_type: WinType) -> FrameChannelT:
+    """
+    ESH synthesis for one channel.
+
+    Parameters
+    ----------
+    X_esh : FrameChannelF
+        MDCT coefficients for 8 short windows (expected shape: (128, 8)).
+    win_type : WinType
+        Window family ("KBD" or "SIN").
+
+    Returns
+    -------
+    FrameChannelT
+        Time-domain channel contribution, shape (2048,).
+        This is already overlap-added internally for the 8 short blocks and
+        ready for OLA at the caller level.
+    """
+    if X_esh.shape != (128, 8):
+        raise ValueError("X_esh must have shape (128, 8).")
+
+    wS = _short_window(win_type)  # (256,)
+    out = np.zeros(2048, dtype=np.float64)
+
+    # Each short IMDCT returns 256 samples. Place them at:
+    # start = 448 + 128*j, j=0..7 (50% overlap)
+    for j in range(8):
+        seg = _imdct(X_esh[:, j]) * wS  # (256,)
+        start = 448 + 128 * j
+        out[start:start + 256] += seg
+
+    return out
+
+
+# -----------------------------------------------------------------------------
+# Public Function prototypes (Level 1)
+# -----------------------------------------------------------------------------
+
+def aac_filter_bank(frame_T: FrameT, frame_type: FrameType, win_type: WinType) -> FrameF:
+    """
+    Filterbank stage (MDCT analysis).
+
+    Parameters
+    ----------
+    frame_T : FrameT
+        Time-domain frame, stereo, shape (2048, 2).
+    frame_type : FrameType
+        Type of the frame under encoding ("OLS"|"LSS"|"ESH"|"LPS").
+    win_type : WinType
+        Window type ("KBD" or "SIN") used for the current frame.
+
+    Returns
+    -------
+    frame_F : FrameF
+        Frequency-domain MDCT coefficients:
+        - If frame_type in {"OLS","LSS","LPS"}: array shape (1024, 2)
+          containing MDCT coefficients for both channels.
+        - If frame_type == "ESH": contains 8 subframes, each subframe has shape (128,2),
+          placed in columns according to subframe order, i.e. overall shape (128, 16).
+    """
+    if frame_T.shape != (2048, 2):
+        raise ValueError("frame_T must have shape (2048, 2).")
+
+    xL :FrameChannelT = frame_T[:, 0].astype(np.float64, copy=False)
+    xR :FrameChannelT = frame_T[:, 1].astype(np.float64, copy=False)
+
+    if frame_type in ("OLS", "LSS", "LPS"):
+        w = _window_sequence(frame_type, win_type)  # length 2048
+        XL = _mdct(xL * w)  # length 1024
+        XR = _mdct(xR * w)  # length 1024
+        out = np.empty((1024, 2), dtype=np.float64)
+        out[:, 0] = XL
+        out[:, 1] = XR
+        return out
+
+    if frame_type == "ESH":
+        Xl = _filter_bank_esh_channel(xL, win_type)  # (128, 8)
+        Xr = _filter_bank_esh_channel(xR, win_type)  # (128, 8)
+
+        # Pack into (128, 16): each subframe as (128,2) placed in columns
+        out = np.empty((128, 16), dtype=np.float64)
+        for j in range(8):
+            out[:, 2 * j + 0] = Xl[:, j]
+            out[:, 2 * j + 1] = Xr[:, j]
+        return out
+
+    raise ValueError(f"Invalid frame_type: {frame_type!r}")
+
+
+def aac_i_filter_bank(frame_F: FrameF, frame_type: FrameType, win_type: WinType) -> FrameT:
+    """
+    Inverse filterbank (IMDCT synthesis).
+
+    Parameters
+    ----------
+    frame_F : FrameF
+        Frequency-domain MDCT coefficients as produced by filter_bank().
+    frame_type : FrameType
+        Frame type ("OLS"|"LSS"|"ESH"|"LPS").
+    win_type : WinType
+        Window type ("KBD" or "SIN").
+
+    Returns
+    -------
+    frame_T : FrameT
+        Reconstructed time-domain frame, stereo, shape (2048, 2).
+    """
+    if frame_type in ("OLS", "LSS", "LPS"):
+        if frame_F.shape != (1024, 2):
+            raise ValueError("For OLS/LSS/LPS, frame_F must have shape (1024, 2).")
+
+        w = _window_sequence(frame_type, win_type)
+
+        xL = _imdct(frame_F[:, 0]) * w
+        xR = _imdct(frame_F[:, 1]) * w
+
+        out = np.empty((2048, 2), dtype=np.float64)
+        out[:, 0] = xL
+        out[:, 1] = xR
+        return out
+
+    if frame_type == "ESH":
+        if frame_F.shape != (128, 16):
+            raise ValueError("For ESH, frame_F must have shape (128, 16).")
+
+        Xl, Xr = _unpack_esh(frame_F)
+        xL = _i_filter_bank_esh_channel(Xl, win_type)
+        xR = _i_filter_bank_esh_channel(Xr, win_type)
+
+        out = np.empty((2048, 2), dtype=np.float64)
+        out[:, 0] = xL
+        out[:, 1] = xR
+        return out
+
+    raise ValueError(f"Invalid frame_type: {frame_type!r}")

source/level_2/core/aac_snr_db.py

@@ -0,0 +1,60 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - SNR dB calculator
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   This module implements SNR calculation in dB
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from core.aac_types import StereoSignal
+import numpy as np
+
+def snr_db(x_ref: StereoSignal, x_hat: StereoSignal) -> float:
+    """
+    Compute overall SNR (dB) over all samples and channels after aligning lengths.
+
+    Parameters
+    ----------
+    x_ref : StereoSignal
+        Reference stereo stream.
+    x_hat : StereoSignal
+        Reconstructed stereo stream.
+
+    Returns
+    -------
+    float
+        SNR in dB.
+        - Returns +inf if noise power is zero.
+        - Returns -inf if signal power is zero.
+    """
+    x_ref = np.asarray(x_ref, dtype=np.float64)
+    x_hat = np.asarray(x_hat, dtype=np.float64)
+
+    if x_ref.ndim == 1:
+        x_ref = x_ref.reshape(-1, 1)
+    if x_hat.ndim == 1:
+        x_hat = x_hat.reshape(-1, 1)
+
+    n = min(x_ref.shape[0], x_hat.shape[0])
+    c = min(x_ref.shape[1], x_hat.shape[1])
+
+    x_ref = x_ref[:n, :c]
+    x_hat = x_hat[:n, :c]
+
+    err = x_ref - x_hat
+    ps = float(np.sum(x_ref * x_ref))
+    pn = float(np.sum(err * err))
+
+    if pn <= 0.0:
+        return float("inf")
+    if ps <= 0.0:
+        return float("-inf")
+
+    return float(10.0 * np.log10(ps / pn))

source/level_2/core/aac_ssc.py

@@ -0,0 +1,217 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - Sequence Segmentation Control module
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   Sequence Segmentation Control module (SSC).
+#   Selects and returns the frame type based on input parameters.
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from typing import Dict, Tuple
+from core.aac_types import FrameType, FrameT, FrameChannelT
+
+import numpy as np
+
+# -----------------------------------------------------------------------------
+# Private helpers for SSC
+# -----------------------------------------------------------------------------
+
+# See Table 1 in mm-2025-hw-v0.1.pdf
+STEREO_MERGE_TABLE: Dict[Tuple[FrameType, FrameType], FrameType] = {
+    ("OLS", "OLS"): "OLS",
+    ("OLS", "LSS"): "LSS",
+    ("OLS", "ESH"): "ESH",
+    ("OLS", "LPS"): "LPS",
+    ("LSS", "OLS"): "LSS",
+    ("LSS", "LSS"): "LSS",
+    ("LSS", "ESH"): "ESH",
+    ("LSS", "LPS"): "ESH",
+    ("ESH", "OLS"): "ESH",
+    ("ESH", "LSS"): "ESH",
+    ("ESH", "ESH"): "ESH",
+    ("ESH", "LPS"): "ESH",
+    ("LPS", "OLS"): "LPS",
+    ("LPS", "LSS"): "ESH",
+    ("LPS", "ESH"): "ESH",
+    ("LPS", "LPS"): "LPS",
+}
+
+
+def _detect_attack(next_frame_channel: FrameChannelT) -> bool:
+    """
+    Detect whether the *next* frame (single channel) implies an attack, i.e. ESH
+    according to the assignment's criterion.
+
+    Parameters
+    ----------
+    next_frame_channel : FrameChannelT
+        One channel of next_frame_T (expected shape: (2048,)).
+
+    Returns
+    -------
+    bool
+        True if an attack is detected (=> next frame predicted ESH), else False.
+
+    Notes
+    -----
+    The criterion is implemented as described in the spec:
+
+    1) Apply the high-pass filter:
+           H(z) = (1 - z^-1) / (1 - 0.5 z^-1)
+       implemented in the time domain as:
+           y[n] = x[n] - x[n-1] + 0.5*y[n-1]
+
+    2) Split y into 16 segments of length 128 and compute segment energies s[l].
+
+    3) Compute the ratio:
+           ds[l] = s[l] / s[l-1]
+
+    4) An attack exists if there exists l in {1..7} such that:
+           s[l] > 1e-3  and  ds[l] > 10
+    """
+    # Local alias; expected to be a 1-D array of length 2048.
+    x = next_frame_channel
+
+    # High-pass filter reference implementation (scalar recurrence).
+    y = np.zeros_like(x)
+    prev_x = 0.0
+    prev_y = 0.0
+    for n in range(x.shape[0]):
+        xn = float(x[n])
+        yn = (xn - prev_x) + 0.5 * prev_y
+        y[n] = yn
+        prev_x = xn
+        prev_y = yn
+
+    # Segment energies over 16 blocks of 128 samples.
+    s = np.empty(16, dtype=np.float64)
+    for l in range(16):
+        a = l * 128
+        b = (l + 1) * 128
+        seg = y[a:b]
+        s[l] = float(np.sum(seg * seg))
+
+    # ds[l] for l>=1. For l=0 not defined, keep 0.
+    ds = np.zeros(16, dtype=np.float64)
+    eps = 1e-12  # Avoid division by zero without materially changing the logic.
+    for l in range(1, 16):
+        ds[l] = s[l] / max(s[l - 1], eps)
+
+    # Spec: check l in {1..7}.
+    for l in range(1, 8):
+        if (s[l] > 1e-3) and (ds[l] > 10.0):
+            return True
+
+    return False
+
+
+def _decide_frame_type(prev_frame_type: FrameType, attack: bool) -> FrameType:
+    """
+    Decide the current frame type for a single channel based on the previous
+    frame type and whether the next frame is predicted to be ESH.
+
+    Rules (spec):
+
+    - If prev is "LSS" => current is "ESH"
+    - If prev is "LPS" => current is "OLS"
+    - If prev is "OLS" => current is "LSS" if attack else "OLS"
+    - If prev is "ESH" => current is "ESH" if attack else "LPS"
+
+    Parameters
+    ----------
+    prev_frame_type : FrameType
+        Previous frame type (one of "OLS", "LSS", "ESH", "LPS").
+    attack : bool
+        True if the next frame is predicted ESH for this channel.
+
+    Returns
+    -------
+    FrameType
+        The per-channel decision for the current frame.
+
+    """
+    if prev_frame_type == "LSS":
+        return "ESH"
+    if prev_frame_type == "LPS":
+        return "OLS"
+    if prev_frame_type == "OLS":
+        return "LSS" if attack else "OLS"
+    if prev_frame_type == "ESH":
+        return "ESH" if attack else "LPS"
+
+    raise ValueError(f"Invalid prev_frame_type: {prev_frame_type!r}")
+
+
+def _stereo_merge(ft_l: FrameType, ft_r: FrameType) -> FrameType:
+    """
+    Merge per-channel frame type decisions into one common frame type using
+    the stereo merge table from the spec.
+
+    Parameters
+    ----------
+    ft_l : FrameType
+        Frame type decision for the left channel.
+    ft_r : FrameType
+        Frame type decision for the right channel.
+
+    Returns
+    -------
+    FrameType
+        The merged common frame type.
+    """
+    try:
+        return STEREO_MERGE_TABLE[(ft_l, ft_r)]
+    except KeyError as e:
+        raise ValueError(f"Invalid stereo merge pair: {(ft_l, ft_r)}") from e
+
+
+# -----------------------------------------------------------------------------
+# Public Function prototypes (Level 1)
+# -----------------------------------------------------------------------------
+
+def aac_SSC(frame_T: FrameT, next_frame_T: FrameT, prev_frame_type: FrameType) -> FrameType:
+    """
+    Sequence Segmentation Control (SSC).
+
+    Select and return the frame type for the current frame (i) based on:
+    - the current time-domain frame (stereo),
+    - the next time-domain frame (stereo), used for attack detection,
+    - the previous frame type.
+
+    Parameters
+    ----------
+    frame_T : FrameT
+        Current time-domain frame i (expected shape: (2048, 2)).
+    next_frame_T : FrameT
+        Next time-domain frame (i+1), used to decide transitions to/from ESH
+        (expected shape: (2048, 2)).
+    prev_frame_type : FrameType
+        Frame type chosen for the previous frame (i-1).
+
+    Returns
+    -------
+    FrameType
+        One of: "OLS", "LSS", "ESH", "LPS".
+    """
+    if frame_T.shape != (2048, 2):
+        raise ValueError("frame_T must have shape (2048, 2).")
+    if next_frame_T.shape != (2048, 2):
+        raise ValueError("next_frame_T must have shape (2048, 2).")
+
+    # Detect attack independently per channel on the next frame.
+    attack_l = _detect_attack(next_frame_T[:, 0])
+    attack_r = _detect_attack(next_frame_T[:, 1])
+
+    # Decide per-channel type based on shared prev_frame_type.
+    ft_l = _decide_frame_type(prev_frame_type, attack_l)
+    ft_r = _decide_frame_type(prev_frame_type, attack_r)
+
+    # Stereo merge as per the spec table.
+    return _stereo_merge(ft_l, ft_r)

source/level_2/core/aac_tns.py

@@ -0,0 +1,549 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - Temporal Noise Shaping (TNS)
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   Temporal Noise Shaping (TNS) module (Level 2).
+#
+#   Public API:
+#     frame_F_out, tns_coeffs = aac_tns(frame_F_in, frame_type)
+#     frame_F_out = aac_i_tns(frame_F_in, frame_type, tns_coeffs)
+#
+#   Notes (per assignment):
+#   - TNS is applied per channel (not stereo).
+#   - For ESH, TNS is applied independently to each of the 8 short subframes.
+#   - Bark band tables are taken from TableB.2.1.9a (long) and TableB.2.1.9b (short)
+#     provided in TableB219.mat.
+#   - Predictor order is fixed to p = 4.
+#   - Coefficients are quantized with a 4-bit uniform symmetric quantizer, step = 0.1.
+#   - Forward TNS applies FIR: H_TNS(z) = 1 - a1 z^-1 - ... - ap z^-p
+#   - Inverse TNS applies the inverse IIR filter using the same quantized coefficients.
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+from scipy.io import loadmat
+
+from core.aac_configuration import PRED_ORDER, QUANT_STEP, QUANT_MAX
+from core.aac_types import *
+
+# -----------------------------------------------------------------------------
+# Private helpers
+# -----------------------------------------------------------------------------
+
+_B219_CACHE: dict[str, FloatArray] | None = None
+
+
+def _load_b219_tables() -> dict[str, FloatArray]:
+    """
+    Load TableB219.mat and cache the contents.
+
+    The project layout guarantees that a 'material' directory is discoverable
+    from the current working directory (tests and level_123 entrypoints).
+
+    Returns
+    -------
+    dict[str, FloatArray]
+        Keys:
+        - "B219a": long bands table (for K=1024 MDCT lines)
+        - "B219b": short bands table (for K=128 MDCT lines)
+    """
+    global _B219_CACHE
+    if _B219_CACHE is not None:
+        return _B219_CACHE
+
+    mat_path = Path("material") / "TableB219.mat"
+    if not mat_path.exists():
+        raise FileNotFoundError("Could not locate material/TableB219.mat in the current working directory.")
+
+    d = loadmat(str(mat_path))
+    if "B219a" not in d or "B219b" not in d:
+        raise ValueError("TableB219.mat missing required variables B219a and/or B219b.")
+
+    _B219_CACHE = {
+        "B219a": np.asarray(d["B219a"], dtype=np.float64),
+        "B219b": np.asarray(d["B219b"], dtype=np.float64),
+    }
+    return _B219_CACHE
+
+
+def _band_ranges_for_kcount(k_count: int) -> BandRanges:
+    """
+    Return Bark band index ranges [start, end] (inclusive) for the given MDCT line count.
+
+    Parameters
+    ----------
+    k_count : int
+        Number of MDCT lines:
+        - 1024 for long frames
+        - 128 for short subframes (ESH)
+
+    Returns
+    -------
+    BandRanges (list[tuple[int, int]])
+        Each tuple is (start_k, end_k) inclusive.
+    """
+    tables = _load_b219_tables()
+    if k_count == 1024:
+        tbl = tables["B219a"]
+    elif k_count == 128:
+        tbl = tables["B219b"]
+    else:
+        raise ValueError("TNS supports only k_count=1024 (long) or k_count=128 (short).")
+
+    start = tbl[:, 1].astype(int)
+    end = tbl[:, 2].astype(int)
+
+    ranges: list[tuple[int, int]] = [(int(s), int(e)) for s, e in zip(start, end)]
+
+    for s, e in ranges:
+        if s < 0 or e < s or e >= k_count:
+            raise ValueError("Invalid band table ranges for given k_count.")
+    return ranges
+
+
+# -----------------------------------------------------------------------------
+# Core DSP helpers
+# -----------------------------------------------------------------------------
+
+def _smooth_sw_inplace(sw: MdctCoeffs) -> None:
+    """
+    Smooth Sw(k) to reduce discontinuities between adjacent Bark bands.
+
+    The assignment applies two passes:
+    - Backward: Sw(k) = (Sw(k) + Sw(k+1))/2
+    - Forward:  Sw(k) = (Sw(k) + Sw(k-1))/2
+
+    Parameters
+    ----------
+    sw : MdctCoeffs
+        1-D array of length K (float64). Modified in-place.
+    """
+    k_count = int(sw.shape[0])
+
+    for k in range(k_count - 2, -1, -1):
+        sw[k] = 0.5 * (sw[k] + sw[k + 1])
+
+    for k in range(1, k_count):
+        sw[k] = 0.5 * (sw[k] + sw[k - 1])
+
+
+def _compute_sw(x: MdctCoeffs) -> MdctCoeffs:
+    """
+    Compute Sw(k) from band energies P(j) and apply boundary smoothing.
+
+    Parameters
+    ----------
+    x : MdctCoeffs
+        1-D MDCT line array, length K.
+
+    Returns
+    -------
+    MdctCoeffs
+        Sw(k), 1-D array of length K, float64.
+    """
+    x = np.asarray(x, dtype=np.float64).reshape(-1)
+    k_count = int(x.shape[0])
+
+    bands = _band_ranges_for_kcount(k_count)
+    sw = np.zeros(k_count, dtype=np.float64)
+
+    for s, e in bands:
+        seg = x[s : e + 1]
+        p_j = float(np.sum(seg * seg))
+        sw_val = float(np.sqrt(p_j))
+        sw[s : e + 1] = sw_val
+
+    _smooth_sw_inplace(sw)
+    return sw
+
+
+def _autocorr(x: MdctCoeffs, p: int) -> MdctCoeffs:
+    """
+    Autocorrelation r(m) for m=0..p.
+
+    Parameters
+    ----------
+    x : MdctCoeffs
+        1-D signal.
+    p : int
+        Maximum lag.
+
+    Returns
+    -------
+    MdctCoeffs
+        r, shape (p+1,), float64.
+    """
+    x = np.asarray(x, dtype=np.float64).reshape(-1)
+    n = int(x.shape[0])
+
+    r = np.zeros(p + 1, dtype=np.float64)
+    for m in range(p + 1):
+        r[m] = float(np.dot(x[m:], x[: n - m]))
+    return r
+
+
+def _lpc_coeffs(xw: MdctCoeffs, p: int) -> MdctCoeffs:
+    """
+    Solve Yule-Walker normal equations for LPC coefficients of order p.
+
+    Parameters
+    ----------
+    xw : MdctCoeffs
+        1-D normalized sequence Xw(k).
+    p : int
+        Predictor order.
+
+    Returns
+    -------
+    MdctCoeffs
+        LPC coefficients a[0..p-1], shape (p,), float64.
+    """
+    r = _autocorr(xw, p)
+
+    R = np.empty((p, p), dtype=np.float64)
+    for i in range(p):
+        for j in range(p):
+            R[i, j] = r[abs(i - j)]
+
+    rhs = r[1 : p + 1].reshape(p)
+
+    reg = 1e-12
+    R_reg = R + reg * np.eye(p, dtype=np.float64)
+
+    a = np.linalg.solve(R_reg, rhs)
+    return a
+
+
+def _quantize_coeffs(a: MdctCoeffs) -> MdctCoeffs:
+    """
+    Quantize LPC coefficients with uniform symmetric quantizer and clamp.
+
+    Parameters
+    ----------
+    a : MdctCoeffs
+        LPC coefficient array, shape (p,).
+
+    Returns
+    -------
+    MdctCoeffs
+        Quantized coefficients, shape (p,), float64.
+    """
+    a = np.asarray(a, dtype=np.float64).reshape(-1)
+    q = np.round(a / QUANT_STEP) * QUANT_STEP
+    q = np.clip(q, -QUANT_MAX, QUANT_MAX)
+    return q.astype(np.float64, copy=False)
+
+
+def _is_inverse_stable(a_q: MdctCoeffs) -> bool:
+    """
+    Check stability of the inverse TNS filter H_TNS^{-1}.
+
+    Forward filter:
+        H_TNS(z) = 1 - a1 z^-1 - ... - ap z^-p
+
+    Inverse filter poles are roots of:
+        A(z) = 1 - a1 z^-1 - ... - ap z^-p
+    Multiply by z^p:
+        z^p - a1 z^{p-1} - ... - ap = 0
+
+    Stability condition:
+        all roots satisfy |z| < 1.
+
+    Parameters
+    ----------
+    a_q : MdctCoeffs
+        Quantized predictor coefficients, shape (p,).
+
+    Returns
+    -------
+    bool
+        True if stable, else False.
+    """
+    a_q = np.asarray(a_q, dtype=np.float64).reshape(-1)
+    p = int(a_q.shape[0])
+
+    # Polynomial in z: z^p - a1 z^{p-1} - ... - ap
+    poly = np.empty(p + 1, dtype=np.float64)
+    poly[0] = 1.0
+    poly[1:] = -a_q
+
+    roots = np.roots(poly)
+
+    # Strictly inside unit circle for stability. Add tiny margin for numeric safety.
+    margin = 1e-12
+    return bool(np.all(np.abs(roots) < (1.0 - margin)))
+
+
+def _stabilize_quantized_coeffs(a_q: MdctCoeffs) -> MdctCoeffs:
+    """
+    Make quantized predictor coefficients stable for inverse filtering.
+
+    Policy:
+    - If already stable: return as-is.
+    - Else: iteratively shrink coefficients by gamma and re-quantize to the 0.1 grid.
+    - If still unstable after attempts: fall back to all-zero coefficients (disable TNS).
+
+    Parameters
+    ----------
+    a_q : MdctCoeffs
+        Quantized predictor coefficients, shape (p,).
+
+    Returns
+    -------
+    MdctCoeffs
+        Stable quantized coefficients, shape (p,).
+    """
+    a_q = np.asarray(a_q, dtype=np.float64).reshape(-1)
+
+    if _is_inverse_stable(a_q):
+        return a_q
+
+    # Try a few shrinking factors. Re-quantize after shrinking to keep coefficients on-grid.
+    gammas = (0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1)
+
+    for g in gammas:
+        cand = _quantize_coeffs(g * a_q)
+        if _is_inverse_stable(cand):
+            return cand
+
+    # Last resort: disable TNS for this vector
+    return np.zeros_like(a_q, dtype=np.float64)
+
+
+def _apply_tns_fir(x: MdctCoeffs, a_q: MdctCoeffs) -> MdctCoeffs:
+    """
+    Apply forward TNS FIR filter:
+        y[k] = x[k] - sum_{l=1..p} a_l * x[k-l]
+
+    Parameters
+    ----------
+    x : MdctCoeffs
+        1-D MDCT lines, length K.
+    a_q : MdctCoeffs
+        Quantized LPC coefficients, shape (p,).
+
+    Returns
+    -------
+    MdctCoeffs
+        Filtered MDCT lines y, length K.
+    """
+    x = np.asarray(x, dtype=np.float64).reshape(-1)
+    a_q = np.asarray(a_q, dtype=np.float64).reshape(-1)
+    p = int(a_q.shape[0])
+    k_count = int(x.shape[0])
+
+    y = np.zeros(k_count, dtype=np.float64)
+    for k in range(k_count):
+        acc = x[k]
+        for l in range(1, p + 1):
+            if k - l >= 0:
+                acc -= a_q[l - 1] * x[k - l]
+        y[k] = acc
+    return y
+
+
+def _apply_itns_iir(y: MdctCoeffs, a_q: MdctCoeffs) -> MdctCoeffs:
+    """
+    Apply inverse TNS IIR filter:
+        x_hat[k] = y[k] + sum_{l=1..p} a_l * x_hat[k-l]
+
+    Parameters
+    ----------
+    y : MdctCoeffs
+        1-D MDCT lines after TNS, length K.
+    a_q : MdctCoeffs
+        Quantized LPC coefficients, shape (p,).
+
+    Returns
+    -------
+    MdctCoeffs
+        Reconstructed MDCT lines x_hat, length K.
+    """
+    y = np.asarray(y, dtype=np.float64).reshape(-1)
+    a_q = np.asarray(a_q, dtype=np.float64).reshape(-1)
+    p = int(a_q.shape[0])
+    k_count = int(y.shape[0])
+
+    x_hat = np.zeros(k_count, dtype=np.float64)
+    for k in range(k_count):
+        acc = y[k]
+        for l in range(1, p + 1):
+            if k - l >= 0:
+                acc += a_q[l - 1] * x_hat[k - l]
+        x_hat[k] = acc
+    return x_hat
+
+
+def _tns_one_vector(x: MdctCoeffs) -> tuple[MdctCoeffs, MdctCoeffs]:
+    """
+    TNS for a single MDCT vector (one long frame or one short subframe).
+
+    Steps:
+    1) Compute Sw(k) from Bark band energies and smooth it.
+    2) Normalize: Xw(k) = X(k) / Sw(k) (safe when Sw=0).
+    3) Compute LPC coefficients (order p=PRED_ORDER) on Xw.
+    4) Quantize coefficients (4-bit symmetric, step QUANT_STEP).
+    5) Apply FIR filter on original X(k) using quantized coefficients.
+
+    Parameters
+    ----------
+    x : MdctCoeffs
+        1-D MDCT vector.
+
+    Returns
+    -------
+    y : MdctCoeffs
+        TNS-processed MDCT vector (same length).
+    a_q : MdctCoeffs
+        Quantized LPC coefficients, shape (PRED_ORDER,).
+    """
+    x = np.asarray(x, dtype=np.float64).reshape(-1)
+    sw = _compute_sw(x)
+
+    eps = 1e-12
+    xw = np.where(sw > eps, x / sw, 0.0)
+
+    a = _lpc_coeffs(xw, PRED_ORDER)
+    a_q = _quantize_coeffs(a)
+
+    # Ensure inverse stability (assignment requirement)
+    a_q = _stabilize_quantized_coeffs(a_q)
+
+    y = _apply_tns_fir(x, a_q)
+    return y, a_q
+
+
+
+# -----------------------------------------------------------------------------
+# Public Functions (Level 2)
+# -----------------------------------------------------------------------------
+
+def aac_tns(frame_F_in: FrameChannelF, frame_type: FrameType) -> Tuple[FrameChannelF, TnsCoeffs]:
+    """
+    Temporal Noise Shaping (TNS) for ONE channel.
+
+    Parameters
+    ----------
+    frame_F_in : FrameChannelF
+        Per-channel MDCT coefficients.
+        Expected (typical) shapes:
+        - If frame_type == "ESH": (128, 8)
+        - Else: (1024, 1) or (1024,)
+
+    frame_type : FrameType
+        Frame type code ("OLS", "LSS", "ESH", "LPS").
+
+    Returns
+    -------
+    frame_F_out : FrameChannelF
+        Per-channel MDCT coefficients after applying TNS.
+        Same shape convention as input.
+
+    tns_coeffs : TnsCoeffs
+        Quantized TNS predictor coefficients.
+        Expected shapes:
+        - If frame_type == "ESH": (PRED_ORDER, 8)
+        - Else: (PRED_ORDER, 1)
+    """
+    x = np.asarray(frame_F_in, dtype=np.float64)
+
+    if frame_type == "ESH":
+        if x.shape != (128, 8):
+            raise ValueError("For ESH, frame_F_in must have shape (128, 8).")
+
+        y = np.empty_like(x, dtype=np.float64)
+        a_out = np.empty((PRED_ORDER, 8), dtype=np.float64)
+
+        for j in range(8):
+            y[:, j], a_out[:, j] = _tns_one_vector(x[:, j])
+
+        return y, a_out
+
+    if x.shape == (1024,):
+        x_vec = x
+        out_shape = (1024,)
+    elif x.shape == (1024, 1):
+        x_vec = x[:, 0]
+        out_shape = (1024, 1)
+    else:
+        raise ValueError('For non-ESH, frame_F_in must have shape (1024,) or (1024, 1).')
+
+    y_vec, a_q = _tns_one_vector(x_vec)
+
+    if out_shape == (1024,):
+        y_out = y_vec
+    else:
+        y_out = y_vec.reshape(1024, 1)
+
+    a_out = a_q.reshape(PRED_ORDER, 1)
+    return y_out, a_out
+
+
+def aac_i_tns(frame_F_in: FrameChannelF, frame_type: FrameType, tns_coeffs: TnsCoeffs) -> FrameChannelF:
+    """
+    Inverse Temporal Noise Shaping (iTNS) for ONE channel.
+
+    Parameters
+    ----------
+    frame_F_in : FrameChannelF
+        Per-channel MDCT coefficients after TNS.
+        Expected (typical) shapes:
+        - If frame_type == "ESH": (128, 8)
+        - Else: (1024, 1) or (1024,)
+
+    frame_type : FrameType
+        Frame type code ("OLS", "LSS", "ESH", "LPS").
+
+    tns_coeffs : TnsCoeffs
+        Quantized TNS predictor coefficients.
+        Expected shapes:
+        - If frame_type == "ESH": (PRED_ORDER, 8)
+        - Else: (PRED_ORDER, 1)
+
+    Returns
+    -------
+    FrameChannelF
+        Per-channel MDCT coefficients after inverse TNS.
+        Same shape convention as input frame_F_in.
+    """
+    x = np.asarray(frame_F_in, dtype=np.float64)
+    a = np.asarray(tns_coeffs, dtype=np.float64)
+
+    if frame_type == "ESH":
+        if x.shape != (128, 8):
+            raise ValueError("For ESH, frame_F_in must have shape (128, 8).")
+        if a.shape != (PRED_ORDER, 8):
+            raise ValueError("For ESH, tns_coeffs must have shape (PRED_ORDER, 8).")
+
+        y = np.empty_like(x, dtype=np.float64)
+        for j in range(8):
+            y[:, j] = _apply_itns_iir(x[:, j], a[:, j])
+        return y
+
+    if a.shape != (PRED_ORDER, 1):
+        raise ValueError("For non-ESH, tns_coeffs must have shape (PRED_ORDER, 1).")
+
+    if x.shape == (1024,):
+        x_vec = x
+        out_shape = (1024,)
+    elif x.shape == (1024, 1):
+        x_vec = x[:, 0]
+        out_shape = (1024, 1)
+    else:
+        raise ValueError('For non-ESH, frame_F_in must have shape (1024,) or (1024, 1).')
+
+    y_vec = _apply_itns_iir(x_vec, a[:, 0])
+
+    if out_shape == (1024,):
+        return y_vec
+    return y_vec.reshape(1024, 1)

source/level_2/core/aac_types.py

@@ -0,0 +1,282 @@
+# ------------------------------------------------------------
+# AAC Coder/Decoder - Public Type Aliases
+#
+# Multimedia course at Aristotle University of
+# Thessaloniki (AUTh)
+#
+# Author:
+#   Christos Choutouridis (ΑΕΜ 8997)
+#   cchoutou@ece.auth.gr
+#
+# Description:
+#   This module implements Public Type aliases
+# ------------------------------------------------------------
+from __future__ import annotations
+
+from typing import List, Literal, TypeAlias, TypedDict
+import numpy as np
+from numpy.typing import NDArray
+
+# -----------------------------------------------------------------------------
+# Code enums (for readability; not intended to enforce shapes/lengths)
+# -----------------------------------------------------------------------------
+
+FrameType: TypeAlias = Literal["OLS", "LSS", "ESH", "LPS"]
+"""
+Frame type codes (AAC):
+- "OLS": ONLY_LONG_SEQUENCE
+- "LSS": LONG_START_SEQUENCE
+- "ESH": EIGHT_SHORT_SEQUENCE
+- "LPS": LONG_STOP_SEQUENCE
+"""
+
+WinType: TypeAlias = Literal["KBD", "SIN"]
+"""
+Window type codes (AAC):
+- "KBD": Kaiser-Bessel-Derived
+- "SIN": sinusoid
+"""
+
+ChannelKey: TypeAlias = Literal["chl", "chr"]
+"""Channel dictionary keys used in Level payloads."""
+
+
+# -----------------------------------------------------------------------------
+# Array “semantic” aliases
+#
+# Goal: communicate meaning (time/frequency/window, stereo/channel) without
+# forcing strict shapes in the type system.
+# -----------------------------------------------------------------------------
+
+FloatArray: TypeAlias = NDArray[np.float64]
+"""
+Generic float64 NumPy array.
+
+Note:
+- We standardize internal numeric computations to float64 for stability and
+  reproducibility. External I/O can still be float32, but we convert at the
+  boundaries.
+"""
+
+Window: TypeAlias = FloatArray
+"""
+Time-domain window (weighting sequence), 1-D.
+
+Typical lengths in this assignment:
+- Long: 2048
+- Short: 256
+- Window sequences for LSS/LPS are also 2048
+
+Expected shape: (N,)
+dtype: float64
+"""
+
+TimeSignal: TypeAlias = FloatArray
+"""
+Time-domain signal samples, typically 1-D.
+
+Examples:
+- Windowed MDCT input: shape (N,)
+- IMDCT output: shape (N,)
+
+dtype: float64
+"""
+
+StereoSignal: TypeAlias = FloatArray
+"""
+Time-domain stereo signal stream.
+
+Expected (typical) shape: (N, 2)
+- axis 0: time samples
+- axis 1: channels [L, R]
+
+dtype: float64
+"""
+
+MdctCoeffs: TypeAlias = FloatArray
+"""
+MDCT coefficient vector, typically 1-D.
+
+Examples:
+- Long: shape (1024,)
+- Short: shape (128,)
+
+dtype: float64
+"""
+
+MdctFrameChannel: TypeAlias = FloatArray
+"""
+Per-channel MDCT container used in Level-1/2 sequences.
+
+Typical shapes:
+- If frame_type in {"OLS","LSS","LPS"}: (1024, 1) or (1024,)
+- If frame_type == "ESH": (128, 8)  (8 short subframes for one channel)
+
+dtype: float64
+
+Notes
+-----
+Some parts of the assignment store long-frame coefficients as a column vector
+(1024, 1) to match MATLAB conventions. Internally you may also use (1024,)
+when convenient, but the semantic meaning is identical.
+"""
+
+TnsCoeffs: TypeAlias = FloatArray
+"""
+Quantized TNS predictor coefficients (one channel).
+
+Typical shapes (Level 2):
+- If frame_type == "ESH": (4, 8)   (order p=4 for each of the 8 short subframes)
+- Else: (4, 1)            (order p=4 for the long frame)
+
+dtype: float64
+
+Notes
+-----
+The assignment uses a 4-bit uniform symmetric quantizer with step size 0.1.
+We store the quantized coefficient values as float64 (typically multiples of 0.1)
+to keep the pipeline simple and readable.
+"""
+
+
+FrameT: TypeAlias = FloatArray
+"""
+Time-domain frame (stereo), as used by the filterbank input/output.
+
+Expected (typical) shape for stereo: (2048, 2)
+- axis 0: time samples
+- axis 1: channels [L, R]
+
+dtype: float64
+"""
+
+FrameChannelT: TypeAlias = FloatArray
+"""
+Time-domain single-channel frame.
+
+Expected (typical) shape: (2048,)
+
+dtype: float64
+"""
+
+FrameF: TypeAlias = FloatArray
+"""
+Frequency-domain frame (MDCT coefficients), stereo container.
+
+Typical shapes (Level 1):
+- If frame_type in {"OLS","LSS","LPS"}: (1024, 2)
+- If frame_type == "ESH": (128, 16)
+
+Rationale for ESH (128, 16):
+- 8 short subframes per channel => 8 * 2 = 16 columns total
+- Each short subframe per stereo is (128, 2), flattened into columns
+  in subframe order: [sf0_L, sf0_R, sf1_L, sf1_R, ..., sf7_L, sf7_R]
+
+dtype: float64
+"""
+
+FrameChannelF: TypeAlias = MdctFrameChannel
+"""
+Frequency-domain single-channel MDCT coefficients.
+
+Typical shapes (Level 1/2):
+- If frame_type in {"OLS","LSS","LPS"}: (1024, 1) or (1024,)
+- If frame_type == "ESH": (128, 8)
+
+dtype: float64
+"""
+
+BandRanges: TypeAlias = list[tuple[int, int]]
+"""
+Bark-band index ranges [start, end] (inclusive) for MDCT lines.
+
+Used by TNS to map MDCT indices k to Bark bands.
+"""
+
+# -----------------------------------------------------------------------------
+# Level 1 AAC sequence payload types
+# -----------------------------------------------------------------------------
+
+class AACChannelFrameF(TypedDict):
+    """
+    Per-channel payload for aac_seq_1[i]["chl"] or ["chr"] (Level 1).
+
+    Keys
+    ----
+    frame_F:
+        The MDCT coefficients for ONE channel.
+        Typical shapes:
+        - ESH: (128, 8)   (8 short subframes)
+        - else: (1024, 1) or (1024,)
+    """
+    frame_F: FrameChannelF
+
+
+class AACSeq1Frame(TypedDict):
+    """
+    One frame dictionary element of aac_seq_1 (Level 1).
+    """
+    frame_type: FrameType
+    win_type: WinType
+    chl: AACChannelFrameF
+    chr: AACChannelFrameF
+
+
+AACSeq1: TypeAlias = List[AACSeq1Frame]
+"""
+AAC sequence for Level 1:
+List of length K (K = number of frames).
+
+Each element is a dict with keys:
+- "frame_type", "win_type", "chl", "chr"
+"""
+
+
+# -----------------------------------------------------------------------------
+# Level 2 AAC sequence payload types (TNS)
+# -----------------------------------------------------------------------------
+
+class AACChannelFrameF2(TypedDict):
+    """
+    Per-channel payload for aac_seq_2[i]["chl"] or ["chr"] (Level 2).
+
+    Keys
+    ----
+    frame_F:
+        The TNS-processed MDCT coefficients for ONE channel.
+        Typical shapes:
+        - ESH: (128, 8)
+        - else: (1024, 1) or (1024,)
+    tns_coeffs:
+        Quantized TNS predictor coefficients for ONE channel.
+        Typical shapes:
+        - ESH: (PRED_ORDER, 8)
+        - else: (PRED_ORDER, 1)
+    """
+    frame_F: FrameChannelF
+    tns_coeffs: TnsCoeffs
+
+
+class AACSeq2Frame(TypedDict):
+    """
+    One frame dictionary element of aac_seq_2 (Level 2).
+    """
+    frame_type: FrameType
+    win_type: WinType
+    chl: AACChannelFrameF2
+    chr: AACChannelFrameF2
+
+
+AACSeq2: TypeAlias = List[AACSeq2Frame]
+"""
+AAC sequence for Level 2:
+List of length K (K = number of frames).
+
+Each element is a dict with keys:
+- "frame_type", "win_type", "chl", "chr"
+
+Level 2 adds:
+- per-channel "tns_coeffs"
+and stores:
+- per-channel "frame_F" after applying TNS.
+"""

source/level_2/level_2.py

@@ -19,3 +19,125 @@
 # ------------------------------------------------------------
 from __future__ import annotations
 
+from pathlib import Path
+from typing import Union
+
+import soundfile as sf
+
+from core.aac_types   import AACSeq2, StereoSignal
+from core.aac_coder   import aac_coder_2 as core_aac_coder_2
+from core.aac_coder   import aac_read_wav_stereo_48k
+from core.aac_decoder import aac_decoder_2 as core_aac_decoder_2
+from core.aac_snr_db  import snr_db
+
+# -----------------------------------------------------------------------------
+# Public Level 2 API (wrappers)
+# -----------------------------------------------------------------------------
+
+def aac_coder_2(filename_in: Union[str, Path]) -> AACSeq2:
+    """
+    Level-2 AAC encoder (wrapper).
+
+    Delegates to core implementation.
+
+    Parameters
+    ----------
+    filename_in : Union[str, Path]
+        Input WAV filename.
+        Assumption: stereo audio, sampling rate 48 kHz.
+
+    Returns
+    -------
+    AACSeq2
+        List of encoded frames (Level 2 schema).
+    """
+    return core_aac_coder_2(filename_in)
+
+
+def i_aac_coder_2(aac_seq_2: AACSeq2, filename_out: Union[str, Path]) -> StereoSignal:
+    """
+    Level-2 AAC decoder (wrapper).
+
+    Delegates to core implementation.
+
+    Parameters
+    ----------
+    aac_seq_2 : AACSeq2
+        Encoded sequence as produced by aac_coder_2().
+    filename_out : Union[str, Path]
+        Output WAV filename. Assumption: 48 kHz, stereo.
+
+    Returns
+    -------
+    StereoSignal
+        Decoded audio samples (time-domain), stereo, shape (N, 2), dtype float64.
+    """
+    return core_aac_decoder_2(aac_seq_2, filename_out)
+
+
+# -----------------------------------------------------------------------------
+# Demo (Level 2)
+# -----------------------------------------------------------------------------
+
+def demo_aac_2(filename_in: Union[str, Path], filename_out: Union[str, Path]) -> float:
+    """
+    Demonstration for the Level-2 codec.
+
+    Runs:
+    - aac_coder_2(filename_in)
+    - aac_decoder_2(aac_seq_2, filename_out)
+    and computes total SNR between original and decoded audio.
+
+    Parameters
+    ----------
+    filename_in : Union[str, Path]
+        Input WAV filename (stereo, 48 kHz).
+    filename_out : Union[str, Path]
+        Output WAV filename (stereo, 48 kHz).
+
+    Returns
+    -------
+    float
+        Overall SNR in dB.
+    """
+    filename_in = Path(filename_in)
+    filename_out = Path(filename_out)
+
+    # Read original audio (reference) with the same validation as the codec.
+    x_ref, fs_ref = aac_read_wav_stereo_48k(filename_in)
+    if int(fs_ref) != 48000:
+        raise ValueError("Input sampling rate must be 48 kHz.")
+
+    # Encode / decode
+    aac_seq_2 = aac_coder_2(filename_in)
+    x_hat = i_aac_coder_2(aac_seq_2, filename_out)
+
+    # Optional sanity: ensure output file exists and is readable
+    _, fs_hat = sf.read(str(filename_out), always_2d=True)
+    if int(fs_hat) != 48000:
+        raise ValueError("Decoded output sampling rate must be 48 kHz.")
+
+    return snr_db(x_ref, x_hat)
+
+
+# -----------------------------------------------------------------------------
+# CLI
+# -----------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    # Example:
+    #   cd level_2
+    #   python -m level_2 input.wav output.wav
+    #   or
+    #   python -m level_2 material/LicorDeCalandraca.wav LicorDeCalandraca_out_l2.wav
+    import sys
+
+    if len(sys.argv) != 3:
+        raise SystemExit("Usage: python -m level_2 <input.wav> <output.wav>")
+
+    in_wav = Path(sys.argv[1])
+    out_wav = Path(sys.argv[2])
+
+    print(f"Encoding/Decoding {in_wav} to {out_wav}")
+    snr = demo_aac_2(in_wav, out_wav)
+    print(f"SNR = {snr:.3f} dB")

source/level_2/material/huff_utils.py

@@ -0,0 +1,400 @@
+import numpy as np
+import scipy.io as sio
+import os
+
+# ------------------ LOAD LUT ------------------
+
+def load_LUT(mat_filename=None):
+    """
+    Loads the list of Huffman Codebooks (LUTs)
+
+    Returns:
+        huffLUT : list (index 1..11 used, index 0 unused)
+    """
+    if mat_filename is None:
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        mat_filename = os.path.join(current_dir, "huffCodebooks.mat")
+
+    mat = sio.loadmat(mat_filename)
+
+
+    huffCodebooks_raw = mat['huffCodebooks'].squeeze()
+
+    huffCodebooks = []
+    for i in range(11):
+        huffCodebooks.append(np.array(huffCodebooks_raw[i]))
+
+    # Build inverse VLC tables
+    invTable = [None] * 11
+
+    for i in range(11):
+        h = huffCodebooks[i][:, 2].astype(int)       # column 3
+        hlength = huffCodebooks[i][:, 1].astype(int) # column 2
+
+        hbin = []
+        for j in range(len(h)):
+            hbin.append(format(h[j], f'0{hlength[j]}b'))
+
+        invTable[i] = vlc_table(hbin)
+
+    # Build Huffman LUT dicts
+    huffLUT = [None] * 12  # index 0 unused
+    params = [
+        (4, 1, True),
+        (4, 1, True),
+        (4, 2, False),
+        (4, 2, False),
+        (2, 4, True),
+        (2, 4, True),
+        (2, 7, False),
+        (2, 7, False),
+        (2, 12, False),
+        (2, 12, False),
+        (2, 16, False),
+    ]
+
+    for i, (nTupleSize, maxAbs, signed) in enumerate(params, start=1):
+        huffLUT[i] = {
+            'LUT': huffCodebooks[i-1],
+            'invTable': invTable[i-1],
+            'codebook': i,
+            'nTupleSize': nTupleSize,
+            'maxAbsCodeVal': maxAbs,
+            'signedValues': signed
+        }
+
+    return huffLUT
+
+def vlc_table(code_array):
+    """
+    codeArray: list of strings, each string is a Huffman codeword (e.g. '0101')
+    returns:
+        h : NumPy array of shape (num_nodes, 3)
+            columns:
+              [ next_if_0 , next_if_1 , symbol_index ]
+    """
+    h = np.zeros((1, 3), dtype=int)
+
+    for code_index, code in enumerate(code_array, start=1):
+        word = [int(bit) for bit in code]
+        h_index = 0
+
+        for bit in word:
+            k = bit
+            next_node = h[h_index, k]
+            if next_node == 0:
+                h = np.vstack([h, [0, 0, 0]])
+                new_index = h.shape[0] - 1
+                h[h_index, k] = new_index
+                h_index = new_index
+            else:
+                h_index = next_node
+
+        h[h_index, 2] = code_index
+
+    return h
+
+# ------------------ ENCODE ------------------
+
+def encode_huff(coeff_sec, huff_LUT_list, force_codebook = None):
+    """
+    Huffman-encode a sequence of quantized coefficients.
+
+    This function selects the appropriate Huffman codebook based on the
+    maximum absolute value of the input coefficients, encodes the coefficients
+    into a binary Huffman bitstream, and returns both the bitstream and the
+    selected codebook index.
+
+    This is the Python equivalent of the MATLAB `encodeHuff.m` function used
+    in audio/image coding (e.g., scale factor band encoding). The input
+    coefficient sequence is grouped into fixed-size tuples as defined by
+    the chosen Huffman LUT. Zero-padding may be applied internally.
+
+    Parameters
+    ----------
+    coeff_sec : array_like of int
+        1-D array of quantized integer coefficients to encode.
+        Typically corresponds to a "section" or scale-factor band.
+
+    huff_LUT_list : list
+        List of Huffman lookup-table dictionaries as returned by `loadLUT()`.
+        Index 1..11 correspond to valid Huffman codebooks.
+        Index 0 is unused.
+
+    Returns
+    -------
+    huffSec : str
+        Huffman-encoded bitstream represented as a string of '0' and '1'
+        characters.
+
+    huffCodebook : int
+        Index (1..11) of the Huffman codebook used for encoding.
+        A value of 0 indicates a special all-zero section.
+    """
+    if force_codebook is not None:
+            return huff_LUT_code_1(huff_LUT_list[force_codebook], coeff_sec)
+    
+    maxAbsVal = np.max(np.abs(coeff_sec))
+
+    if maxAbsVal == 0:
+        huffCodebook = 0
+        huffSec = huff_LUT_code_0()
+
+    elif maxAbsVal == 1:
+        candidates = [1, 2]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal == 2:
+        candidates = [3, 4]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal in (3, 4):
+        candidates = [5, 6]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal in (5, 6, 7):
+        candidates = [7, 8]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal in (8, 9, 10, 11, 12):
+        candidates = [9, 10]
+        huffSec1 = huff_LUT_code_1(huff_LUT_list[candidates[0]], coeff_sec)
+        huffSec2 = huff_LUT_code_1(huff_LUT_list[candidates[1]], coeff_sec)
+        if len(huffSec1) <= len(huffSec2):
+            huffSec = huffSec1
+            huffCodebook = candidates[0]
+        else:
+            huffSec = huffSec2
+            huffCodebook = candidates[1]
+
+    elif maxAbsVal in (13, 14, 15):
+        huffCodebook = 11
+        huffSec = huff_LUT_code_1(huff_LUT_list[huffCodebook], coeff_sec)
+
+    else:
+        huffCodebook = 11
+        huffSec = huff_LUT_code_ESC(huff_LUT_list[huffCodebook], coeff_sec)
+
+    return huffSec, huffCodebook
+
+def huff_LUT_code_1(huff_LUT, coeff_sec):
+    LUT = huff_LUT['LUT']
+    nTupleSize = huff_LUT['nTupleSize']
+    maxAbsCodeVal = huff_LUT['maxAbsCodeVal']
+    signedValues = huff_LUT['signedValues']
+
+    numTuples = int(np.ceil(len(coeff_sec) / nTupleSize))
+
+    if signedValues:
+        coeff = coeff_sec + maxAbsCodeVal
+        base = 2 * maxAbsCodeVal + 1
+    else:
+        coeff = coeff_sec
+        base = maxAbsCodeVal + 1
+
+    coeffPad = np.zeros(numTuples * nTupleSize, dtype=int)
+    coeffPad[:len(coeff)] = coeff
+
+    huffSec = []
+
+    powers = base ** np.arange(nTupleSize - 1, -1, -1)
+
+    for i in range(numTuples):
+        nTuple = coeffPad[i*nTupleSize:(i+1)*nTupleSize]
+        huffIndex = int(np.abs(nTuple) @ powers)
+
+        hexVal = LUT[huffIndex, 2]
+        huffLen = LUT[huffIndex, 1]
+
+        bits = format(int(hexVal), f'0{int(huffLen)}b')
+
+        if signedValues:
+            huffSec.append(bits)
+        else:
+            signBits = ''.join('1' if v < 0 else '0' for v in nTuple)
+            huffSec.append(bits + signBits)
+
+    return ''.join(huffSec)
+
+def huff_LUT_code_0():
+    return ''
+
+def huff_LUT_code_ESC(huff_LUT, coeff_sec):
+    LUT = huff_LUT['LUT']
+    nTupleSize = huff_LUT['nTupleSize']
+    maxAbsCodeVal = huff_LUT['maxAbsCodeVal']
+
+    numTuples = int(np.ceil(len(coeff_sec) / nTupleSize))
+    base = maxAbsCodeVal + 1
+
+    coeffPad = np.zeros(numTuples * nTupleSize, dtype=int)
+    coeffPad[:len(coeff_sec)] = coeff_sec
+
+    huffSec = []
+    powers = base ** np.arange(nTupleSize - 1, -1, -1)
+
+    for i in range(numTuples):
+        nTuple = coeffPad[i*nTupleSize:(i+1)*nTupleSize]
+
+        lnTuple = nTuple.astype(float)
+        lnTuple[lnTuple == 0] = np.finfo(float).eps
+
+        N4 = np.maximum(0, np.floor(np.log2(np.abs(lnTuple))).astype(int))
+        N = np.maximum(0, N4 - 4)
+        esc = np.abs(nTuple) > 15
+
+        nTupleESC = nTuple.copy()
+        nTupleESC[esc] = np.sign(nTupleESC[esc]) * 16
+
+        huffIndex = int(np.abs(nTupleESC) @ powers)
+
+        hexVal = LUT[huffIndex, 2]
+        huffLen = LUT[huffIndex, 1]
+
+        bits = format(int(hexVal), f'0{int(huffLen)}b')
+
+        escSeq = ''
+        for k in range(nTupleSize):
+            if esc[k]:
+                escSeq += '1' * N[k]
+                escSeq += '0'
+                escSeq += format(abs(nTuple[k]) - (1 << N4[k]), f'0{N4[k]}b')
+
+        signBits = ''.join('1' if v < 0 else '0' for v in nTuple)
+        huffSec.append(bits + signBits + escSeq)
+
+    return ''.join(huffSec)
+
+# ------------------ DECODE ------------------
+
+def decode_huff(huff_sec, huff_LUT):
+    """
+    Decode a Huffman-encoded stream.
+
+    Parameters
+    ----------
+    huff_sec : array-like of int or str
+        Huffman encoded stream as a sequence of 0 and 1 (string or list/array).
+    huff_LUT : dict
+        Huffman lookup table with keys:
+            - 'invTable': inverse table (numpy array)
+            - 'codebook': codebook number
+            - 'nTupleSize': tuple size
+            - 'maxAbsCodeVal': maximum absolute code value
+            - 'signedValues': True/False
+
+    Returns
+    -------
+    decCoeffs : list of int
+        Decoded quantized coefficients.
+    """
+
+    h = huff_LUT['invTable']
+    huffCodebook = huff_LUT['codebook']
+    nTupleSize = huff_LUT['nTupleSize']
+    maxAbsCodeVal = huff_LUT['maxAbsCodeVal']
+    signedValues = huff_LUT['signedValues']
+
+    # Convert string to array of ints
+    if isinstance(huff_sec, str):
+        huff_sec = np.array([int(b) for b in huff_sec])
+
+    eos = False
+    decCoeffs = []
+    streamIndex = 0
+
+    while not eos:
+        wordbit = 0
+        r = 0  # start at root
+
+        # Decode Huffman word using inverse table
+        while True:
+            b = huff_sec[streamIndex + wordbit]
+            wordbit += 1
+            rOld = r
+            r = h[rOld, b]
+            if h[r, 0] == 0 and h[r, 1] == 0:
+                symbolIndex = h[r, 2] - 1  # zero-based
+                streamIndex += wordbit
+                break
+
+        # Decode n-tuple magnitudes
+        if signedValues:
+            base = 2 * maxAbsCodeVal + 1
+            nTupleDec = []
+            tmp = symbolIndex
+            for p in reversed(range(nTupleSize)):
+                val = tmp // (base ** p)
+                nTupleDec.append(val - maxAbsCodeVal)
+                tmp = tmp % (base ** p)
+            nTupleDec = np.array(nTupleDec)
+        else:
+            base = maxAbsCodeVal + 1
+            nTupleDec = []
+            tmp = symbolIndex
+            for p in reversed(range(nTupleSize)):
+                val = tmp // (base ** p)
+                nTupleDec.append(val)
+                tmp = tmp % (base ** p)
+            nTupleDec = np.array(nTupleDec)
+
+            # Apply sign bits
+            nTupleSignBits = huff_sec[streamIndex:streamIndex + nTupleSize]
+            nTupleSign = -(np.sign(nTupleSignBits - 0.5))
+            streamIndex += nTupleSize
+            nTupleDec = nTupleDec * nTupleSign
+
+        # Handle escape sequences
+        escIndex = np.where(np.abs(nTupleDec) == 16)[0]
+        if huffCodebook == 11 and escIndex.size > 0:
+            for idx in escIndex:
+                N = 0
+                b = huff_sec[streamIndex]
+                while b:
+                    N += 1
+                    b = huff_sec[streamIndex + N]
+                streamIndex += N
+                N4 = N + 4
+                escape_word = huff_sec[streamIndex:streamIndex + N4]
+                escape_value = 2 ** N4 + int("".join(map(str, escape_word)), 2)
+                nTupleDec[idx] = escape_value
+                streamIndex += N4 + 1
+            # Apply signs again
+            nTupleDec[escIndex] *= nTupleSign[escIndex]
+
+        decCoeffs.extend(nTupleDec.tolist())
+
+        if streamIndex >= len(huff_sec):
+            eos = True
+
+    return decCoeffs
+    
+