ref: 290be25b982380552ced642e51e225c0bbe9985a
parent: 1accd2472e678d540fa024f05da68088014dafaa
author: Jan Buethe <jbuethe@amazon.de>
date: Fri Oct 20 10:24:27 EDT 2023
added 16kHz version of opus_compare in python
--- /dev/null
+++ b/dnn/torch/osce/utils/compare.py
@@ -1,0 +1,90 @@
+import numpy as np
+import scipy.signal
+
+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):
+ """ 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 = re - np.log(re) - 1
+ Eb = ((im @ fb.T) / np.sum(fb, axis=1))
+ Ef = np.mean(Eb ** 2, axis=1)
+ err = np.mean(Ef ** 4, axis=0) ** (1/16)
+
+ return float(err)
\ No newline at end of file
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