vad.py 文件源码

def calculate_features_for_VAD(sound_frames, frequencies_axis, spectrogram):
    features = numpy.empty((spectrogram.shape[0], 3))
    # smooted_spectrogram, smoothed_frequencies_axis = smooth_spectrogram(spectrogram, frequencies_axis, 24)
    for time_ind in range(spectrogram.shape[0]):
        mean_spectrum = spectrogram[time_ind].mean()
        if mean_spectrum > 0.0:
            sfm = -10.0 * math.log10(stats.gmean(spectrogram[time_ind]) / mean_spectrum)
        else:
            sfm = 0.0
        # max_freq = smoothed_frequencies_axis[smooted_spectrogram[time_ind].argmax()]
        max_freq = frequencies_axis[spectrogram[time_ind].argmax()]
        features[time_ind][0] = numpy.square(sound_frames[time_ind]).mean()
        features[time_ind][1] = sfm
        features[time_ind][2] = max_freq
    """medfilt_order = 3
    for feature_ind in range(features.shape[0]):
        features[feature_ind] = signal.medfilt(features[feature_ind], medfilt_order)"""
    return features