""" /* 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, factor=1): """ 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 = [factor * b for b in [0, 2, 4, 6, 7, 9, 11, 13, 15, 18, 22, 26, 31, 36, 43, 51, 60, 75]] window_size = factor * 160 hop_size = factor * 40 num_bins = window_size // 2 + 1 num_bands = len(band_limits) - 1 fb = rect_fb(band_limits, num_bins=num_bins) # trim samples to same size num_samples = min(len(x), len(y)) x = x[:num_samples].copy() * 2**15 y = y[:num_samples].copy() * 2**15 psd_x = power_spectrum(x, window_size=window_size, hop_size=hop_size) + 100000 psd_y = power_spectrum(y, window_size=window_size, hop_size=hop_size) + 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 ** factor) * 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 = re - np.log(re) - 1 im = np.log(re) ** 2 Eb = ((im @ fb.T) / np.sum(fb, axis=1)) Ef = np.mean(Eb ** 1, 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) def compare(x, y, apply_vad=False): err = np.linalg.norm([_compare(x, y, apply_vad=apply_vad, factor=1)], ord=2) return 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}")