def mel2linear(melspectrogram, *, freq_min, freq_max):
"""
Convert mel-frequency into linear-frequency w/o dimension compression (with linear interpolation in linear-frequency domain)
Args:
melspectrogram (numpy.ndarray 2D): magnitude, IF and any other melspectrogram
Returns:
numpy.ndarray 2D: linear-nized spectrogram
"""
time = range(melspectrogram.shape[1])
even_spaced_mel = np.linspace(start=hz2mel(freq_min, htk=True),
stop=hz2mel(freq_max, htk=True),
num=melspectrogram.shape[0],
endpoint=True)
mel_in_freq = [mel2hz(mel, htk=True) for mel in even_spaced_mel]
linearnizer = interp2d(time, mel_in_freq, melspectrogram)
linear_freq = np.linspace(start=freq_min,
stop=freq_max,
num=melspectrogram.shape[0],
endpoint=True)
spectrogram = linearnizer(time, linear_freq)
return spectrogram
def linear2melD(spectrogram, *, freq_min, freq_max):
"""
Convert linear-frequency into mel-frequency w/o dimension compression (with linear interpolation in linear-frequency domain)
Args:
spectrogram (numpy.ndarray 2D): magnitude, IF and any other mel-compatible spectrogram
Returns:
numpy.ndarray 2D: mel-nized spectrogram
"""
linear_freq = np.linspace(start=freq_min,
stop=freq_max,
num=spectrogram.shape[0],
endpoint=True)
time = range(spectrogram.shape[1])
melnizer = interp2d(time, linear_freq, spectrogram)
even_spaced_mel = np.linspace(start=hz2mel(freq_min, htk=True),
stop=hz2mel(freq_max, htk=True),
num=spectrogram.shape[0] * scaler,
endpoint=True)
mel_in_freq = [mel2hz(mel, htk=True) for mel in even_spaced_mel]
mel_spectrogram = melnizer(time, mel_in_freq)
return mel_spectrogram