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import librosa
import numpy as np
from basis import ScoreBasis
class FWSEGSNR(ScoreBasis):
def __init__(self):
super(FWSEGSNR, self).__init__(name='FWSEGSNR')
self.intrusive = False
def windowed_scoring(self, audios, score_rate):
if len(audios) != 2:
return None
return fwsegsnr(audios[1], audios[0], score_rate)
def fwsegsnr(x, y, fs, frame_sz = 0.025, shift_sz= 0.01, win='hann', numband=23):
epsilon = np.finfo(np.float32).eps
frame = int(np.fix(frame_sz * fs))
shift = int(np.fix(shift_sz * fs))
window = win
nband = numband
noverlap = frame - shift
fftpt = int(2**np.ceil(np.log2(np.abs(frame))))
x = x / np.sqrt(sum(np.power(x, 2)))
y = y / np.sqrt(sum(np.power(y, 2)))
assert len(x) == len(y), print('Wav length are not matched!')
X_stft = np.abs(librosa.stft(x, n_fft=fftpt, hop_length=shift, win_length=frame, window=window, center=False))
Y_stft = np.abs(librosa.stft(y, n_fft=fftpt, hop_length=shift, win_length=frame, window=window, center=False))
num_freq = X_stft.shape[0]
num_frame = X_stft.shape[1]
X_mel = librosa.feature.melspectrogram(S=X_stft, sr=fs, n_mels=nband, fmin=0, fmax=fs/2)
Y_mel = librosa.feature.melspectrogram(S=Y_stft, sr=fs, n_mels=nband, fmin=0, fmax=fs/2)
# Calculate SNR.
W = np.power(Y_mel, 0.2)
E = X_mel - Y_mel
E[E == 0.0] = epsilon
E_power = np.power(E, 2)
Y_div_E = np.divide((np.power(Y_mel,2)), (np.power(E,2)))
Y_div_E[Y_div_E==0] = epsilon
ds = 10 * np.divide(np.sum(np.multiply(W, np.log10(Y_div_E)), 1), np.sum(W, 1))
ds[ds > 35] = 35
ds[ds < -10] = -10
d = np.mean(ds)
return d
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