Level_1: File restructure to support centralized development

This commit is contained in:
2026-02-08 17:22:23 +02:00
parent dde11ddebe
commit 8427d0e721
25 changed files with 3990 additions and 1229 deletions
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# ------------------------------------------------------------
# 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_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, fs = aac_read_wav_stereo_48k(filename_in)
_ = fs # kept for clarity; 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
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# ------------------------------------------------------------
# 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
# Window type
# Options: "SIN", "KBD"
WIN_TYPE: WinType = "SIN"
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# ------------------------------------------------------------
# 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_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
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# ------------------------------------------------------------
# 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}")
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# ------------------------------------------------------------
# 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)
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# ------------------------------------------------------------
# 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 1 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
"""
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 = FloatArray
"""
Frequency-domain single-channel frame (MDCT coefficients).
Typical shapes (Level 1):
- If frame_type in {"OLS","LSS","LPS"}: (1024,)
- If frame_type == "ESH": (128, 8) (8 short subframes for one channel)
dtype: float64
"""
# -----------------------------------------------------------------------------
# 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, )
"""
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"
"""
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# ------------------------------------------------------------
# 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"
@@ -0,0 +1,156 @@
# ------------------------------------------------------------
# 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
@@ -0,0 +1,269 @@
# ------------------------------------------------------------
# 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
@@ -0,0 +1,117 @@
# ------------------------------------------------------------
# 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()