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"""
/* Copyright (c) 2023 Amazon
Written by Jan Buethe */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
"""
import numpy as np
import scipy.signal
def compute_vad_mask(x, fs, stop_db=-70):
frame_length = (fs + 49) // 50
x = x[: frame_length * (len(x) // frame_length)]
frames = x.reshape(-1, frame_length)
frame_energy = np.sum(frames ** 2, axis=1)
frame_energy_smooth = np.convolve(frame_energy, np.ones(5) / 5, mode='same')
max_threshold = frame_energy.max() * 10 ** (stop_db/20)
vactive = np.ones_like(frames)
vactive[frame_energy_smooth < max_threshold, :] = 0
vactive = vactive.reshape(-1)
filter = np.sin(np.arange(frame_length) * np.pi / (frame_length - 1))
filter = filter / filter.sum()
mask = np.convolve(vactive, filter, mode='same')
return x, mask
def convert_mask(mask, num_frames, frame_size=160, hop_size=40):
num_samples = frame_size + (num_frames - 1) * hop_size
if len(mask) < num_samples:
mask = np.concatenate((mask, np.zeros(num_samples - len(mask))), dtype=mask.dtype)
else:
mask = mask[:num_samples]
new_mask = np.array([np.mean(mask[i*hop_size : i*hop_size + frame_size]) for i in range(num_frames)])
return new_mask
def power_spectrum(x, window_size=160, hop_size=40, window='hamming'):
num_spectra = (len(x) - window_size - hop_size) // hop_size
window = scipy.signal.get_window(window, window_size)
N = window_size // 2
frames = np.concatenate([x[np.newaxis, i * hop_size : i * hop_size + window_size] for i in range(num_spectra)]) * window
psd = np.abs(np.fft.fft(frames, axis=1)[:, :N + 1]) ** 2
return psd
def frequency_mask(num_bands, up_factor, down_factor):
up_mask = np.zeros((num_bands, num_bands))
down_mask = np.zeros((num_bands, num_bands))
for i in range(num_bands):
up_mask[i, : i + 1] = up_factor ** np.arange(i, -1, -1)
down_mask[i, i :] = down_factor ** np.arange(num_bands - i)
return down_mask @ up_mask
def rect_fb(band_limits, num_bins=None):
num_bands = len(band_limits) - 1
if num_bins is None:
num_bins = band_limits[-1]
fb = np.zeros((num_bands, num_bins))
for i in range(num_bands):
fb[i, band_limits[i]:band_limits[i+1]] = 1
return fb
def compare(x, y, apply_vad=False):
""" Modified version of opus_compare for 16 kHz mono signals
Args:
x (np.ndarray): reference input signal scaled to [-1, 1]
y (np.ndarray): test signal scaled to [-1, 1]
Returns:
float: perceptually weighted error
"""
# filter bank: bark scale with minimum-2-bin bands and cutoff at 7.5 kHz
band_limits = [0, 2, 4, 6, 7, 9, 11, 13, 15, 18, 22, 26, 31, 36, 43, 51, 60, 75]
num_bands = len(band_limits) - 1
fb = rect_fb(band_limits, num_bins=81)
# trim samples to same size
num_samples = min(len(x), len(y))
x = x[:num_samples] * 2**15
y = y[:num_samples] * 2**15
psd_x = power_spectrum(x) + 100000
psd_y = power_spectrum(y) + 100000
num_frames = psd_x.shape[0]
# average band energies
be_x = (psd_x @ fb.T) / np.sum(fb, axis=1)
# frequecy masking
f_mask = frequency_mask(num_bands, 0.1, 0.03)
mask_x = be_x @ f_mask.T
# temporal masking
for i in range(1, num_frames):
mask_x[i, :] += 0.5 * mask_x[i-1, :]
# apply mask
masked_psd_x = psd_x + 0.1 * (mask_x @ fb)
masked_psd_y = psd_y + 0.1 * (mask_x @ fb)
# 2-frame average
masked_psd_x = masked_psd_x[1:] + masked_psd_x[:-1]
masked_psd_y = masked_psd_y[1:] + masked_psd_y[:-1]
# distortion metric
re = masked_psd_y / masked_psd_x
im = np.log(re) ** 2
Eb = ((im @ fb.T) / np.sum(fb, axis=1))
Ef = np.mean(Eb , axis=1)
if apply_vad:
_, mask = compute_vad_mask(x, 16000)
mask = convert_mask(mask, Ef.shape[0])
else:
mask = np.ones_like(Ef)
err = np.mean(np.abs(Ef[mask > 1e-6]) ** 3) ** (1/6)
return float(err)
if __name__ == "__main__":
import argparse
from scipy.io import wavfile
parser = argparse.ArgumentParser()
parser.add_argument('ref', type=str, help='reference wav file')
parser.add_argument('deg', type=str, help='degraded wav file')
parser.add_argument('--apply-vad', action='store_true')
args = parser.parse_args()
fs1, x = wavfile.read(args.ref)
fs2, y = wavfile.read(args.deg)
if max(fs1, fs2) != 16000:
raise ValueError('error: encountered sampling frequency diffrent from 16kHz')
x = x.astype(np.float32) / 2**15
y = y.astype(np.float32) / 2**15
err = compare(x, y, apply_vad=args.apply_vad)
print(f"MOC: {err}")
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