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from basis import ScoreBasis
import numpy as np
class SNR(ScoreBasis):
def __init__(self):
super(SNR, self).__init__(name='SNR')
self.intrusive = False
def windowed_scoring(self, audios, score_rate):
if len(audios) != 2:
raise ValueError('SNR needs a reference and a test signals.')
return cal_SNR(audios[0], audios[1], score_rate)
def cal_SNR(ref_wav, deg_wav, srate=16000, eps=1e-10):
# obtained from https://github.com/wooseok-shin/MetricGAN-plus-pytorch/blob/main/metric_functions/metric_helper.py
""" Segmental Signal-to-Noise Ratio Objective Speech Quality Measure
This function implements the segmental signal-to-noise ratio
as defined in [1, p. 45] (see Equation 2.12).
"""
clean_speech = ref_wav
processed_speech = deg_wav
clean_length = ref_wav.shape[0]
processed_length = deg_wav.shape[0]
# scale both to have same dynamic range. Remove DC too.
clean_speech -= clean_speech.mean()
processed_speech -= processed_speech.mean()
processed_speech *= (np.max(np.abs(clean_speech)) / np.max(np.abs(processed_speech)))
# Signal-to-Noise Ratio
dif = ref_wav - deg_wav
overall_snr = 10 * np.log10(np.sum(ref_wav ** 2) / (np.sum(dif ** 2) + 10e-20))
return overall_snr
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