1""" 2/* Copyright (c) 2023 Amazon 3 Written by Jan Buethe */ 4/* 5 Redistribution and use in source and binary forms, with or without 6 modification, are permitted provided that the following conditions 7 are met: 8 9 - Redistributions of source code must retain the above copyright 10 notice, this list of conditions and the following disclaimer. 11 12 - Redistributions in binary form must reproduce the above copyright 13 notice, this list of conditions and the following disclaimer in the 14 documentation and/or other materials provided with the distribution. 15 16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17 ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER 20 OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 21 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 22 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 23 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 24 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 25 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 26 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27*/ 28""" 29 30import numpy as np 31import scipy.signal 32 33def compute_vad_mask(x, fs, stop_db=-70): 34 35 frame_length = (fs + 49) // 50 36 x = x[: frame_length * (len(x) // frame_length)] 37 38 frames = x.reshape(-1, frame_length) 39 frame_energy = np.sum(frames ** 2, axis=1) 40 frame_energy_smooth = np.convolve(frame_energy, np.ones(5) / 5, mode='same') 41 42 max_threshold = frame_energy.max() * 10 ** (stop_db/20) 43 vactive = np.ones_like(frames) 44 vactive[frame_energy_smooth < max_threshold, :] = 0 45 vactive = vactive.reshape(-1) 46 47 filter = np.sin(np.arange(frame_length) * np.pi / (frame_length - 1)) 48 filter = filter / filter.sum() 49 50 mask = np.convolve(vactive, filter, mode='same') 51 52 return x, mask 53 54def convert_mask(mask, num_frames, frame_size=160, hop_size=40): 55 num_samples = frame_size + (num_frames - 1) * hop_size 56 if len(mask) < num_samples: 57 mask = np.concatenate((mask, np.zeros(num_samples - len(mask))), dtype=mask.dtype) 58 else: 59 mask = mask[:num_samples] 60 61 new_mask = np.array([np.mean(mask[i*hop_size : i*hop_size + frame_size]) for i in range(num_frames)]) 62 63 return new_mask 64 65def power_spectrum(x, window_size=160, hop_size=40, window='hamming'): 66 num_spectra = (len(x) - window_size - hop_size) // hop_size 67 window = scipy.signal.get_window(window, window_size) 68 N = window_size // 2 69 70 frames = np.concatenate([x[np.newaxis, i * hop_size : i * hop_size + window_size] for i in range(num_spectra)]) * window 71 psd = np.abs(np.fft.fft(frames, axis=1)[:, :N + 1]) ** 2 72 73 return psd 74 75 76def frequency_mask(num_bands, up_factor, down_factor): 77 78 up_mask = np.zeros((num_bands, num_bands)) 79 down_mask = np.zeros((num_bands, num_bands)) 80 81 for i in range(num_bands): 82 up_mask[i, : i + 1] = up_factor ** np.arange(i, -1, -1) 83 down_mask[i, i :] = down_factor ** np.arange(num_bands - i) 84 85 return down_mask @ up_mask 86 87 88def rect_fb(band_limits, num_bins=None): 89 num_bands = len(band_limits) - 1 90 if num_bins is None: 91 num_bins = band_limits[-1] 92 93 fb = np.zeros((num_bands, num_bins)) 94 for i in range(num_bands): 95 fb[i, band_limits[i]:band_limits[i+1]] = 1 96 97 return fb 98 99 100def _compare(x, y, apply_vad=False, factor=1): 101 """ Modified version of opus_compare for 16 kHz mono signals 102 103 Args: 104 x (np.ndarray): reference input signal scaled to [-1, 1] 105 y (np.ndarray): test signal scaled to [-1, 1] 106 107 Returns: 108 float: perceptually weighted error 109 """ 110 # filter bank: bark scale with minimum-2-bin bands and cutoff at 7.5 kHz 111 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]] 112 window_size = factor * 160 113 hop_size = factor * 40 114 num_bins = window_size // 2 + 1 115 num_bands = len(band_limits) - 1 116 fb = rect_fb(band_limits, num_bins=num_bins) 117 118 # trim samples to same size 119 num_samples = min(len(x), len(y)) 120 x = x[:num_samples].copy() * 2**15 121 y = y[:num_samples].copy() * 2**15 122 123 psd_x = power_spectrum(x, window_size=window_size, hop_size=hop_size) + 100000 124 psd_y = power_spectrum(y, window_size=window_size, hop_size=hop_size) + 100000 125 126 num_frames = psd_x.shape[0] 127 128 # average band energies 129 be_x = (psd_x @ fb.T) / np.sum(fb, axis=1) 130 131 # frequecy masking 132 f_mask = frequency_mask(num_bands, 0.1, 0.03) 133 mask_x = be_x @ f_mask.T 134 135 # temporal masking 136 for i in range(1, num_frames): 137 mask_x[i, :] += (0.5 ** factor) * mask_x[i-1, :] 138 139 # apply mask 140 masked_psd_x = psd_x + 0.1 * (mask_x @ fb) 141 masked_psd_y = psd_y + 0.1 * (mask_x @ fb) 142 143 # 2-frame average 144 masked_psd_x = masked_psd_x[1:] + masked_psd_x[:-1] 145 masked_psd_y = masked_psd_y[1:] + masked_psd_y[:-1] 146 147 # distortion metric 148 re = masked_psd_y / masked_psd_x 149 #im = re - np.log(re) - 1 150 im = np.log(re) ** 2 151 Eb = ((im @ fb.T) / np.sum(fb, axis=1)) 152 Ef = np.mean(Eb ** 1, axis=1) 153 154 if apply_vad: 155 _, mask = compute_vad_mask(x, 16000) 156 mask = convert_mask(mask, Ef.shape[0]) 157 else: 158 mask = np.ones_like(Ef) 159 160 err = np.mean(np.abs(Ef[mask > 1e-6]) ** 3) ** (1/6) 161 162 return float(err) 163 164def compare(x, y, apply_vad=False): 165 err = np.linalg.norm([_compare(x, y, apply_vad=apply_vad, factor=1)], ord=2) 166 return err 167 168if __name__ == "__main__": 169 import argparse 170 from scipy.io import wavfile 171 172 parser = argparse.ArgumentParser() 173 parser.add_argument('ref', type=str, help='reference wav file') 174 parser.add_argument('deg', type=str, help='degraded wav file') 175 parser.add_argument('--apply-vad', action='store_true') 176 args = parser.parse_args() 177 178 179 fs1, x = wavfile.read(args.ref) 180 fs2, y = wavfile.read(args.deg) 181 182 if max(fs1, fs2) != 16000: 183 raise ValueError('error: encountered sampling frequency diffrent from 16kHz') 184 185 x = x.astype(np.float32) / 2**15 186 y = y.astype(np.float32) / 2**15 187 188 err = compare(x, y, apply_vad=args.apply_vad) 189 190 print(f"MOC: {err}") 191