mgc2sp#

class diffsptk.MelGeneralizedCepstrumToSpectrum(cep_order: int, fft_length: int, *, alpha: float = 0, gamma: float = 0, norm: bool = False, mul: bool = False, n_fft=512, out_format: str | int = 'power')[source]#

See this page for details.

Parameters:

cep_orderint >= 0: The order of the mel-cepstrum, \(M\).
fft_lengthint >= 2: The number of FFT bins, \(L\).
alphafloat in (-1, 1): The frequency warping factor, \(\alpha\).
gammafloat in [-1, 1]: The gamma parameter, \(\gamma\).
normbool: If True, the input is assumed to be normalized.
mulbool: If True, the input is assumed to be gamma-multiplied.
n_fftint >> L: The number of FFT bins. Accurate conversion requires a large value.
out_format[‘db’, ‘log-magnitude’, ‘magnitude’, ‘power’, ‘cycle’, ‘radian’, ‘degree’, ‘complex’]: The output format.

forward(mc: Tensor) → Tensor[source]#

Convert mel-cepstrum to spectrum.

Parameters:

mcTensor [shape=(…, M+1)]: Mel-cepstrum.

Returns:

outTensor [shape=(…, L/2+1)]: Spectrum.

Examples

>>> x = diffsptk.ramp(19)
>>> stft = diffsptk.STFT(frame_length=10, frame_period=10, fft_length=16)
>>> mcep = diffsptk.MelCepstralAnalysis(3, 16, 0.1, n_iter=1)
>>> mc = mcep(stft(x))
>>> mc
tensor([[-0.8851,  0.7917, -0.1737,  0.0175],
        [-0.3522,  4.4222, -1.0882, -0.0511]])
>>> mc2sp = diffsptk.MelGeneralizedCepstrumToSpectrum(3, 8, 0.1)
>>> sp = mc2sp(mc)
>>> sp
tensor([[6.0634e-01, 4.6702e-01, 1.7489e-01, 4.4821e-02, 2.3869e-02],
        [3.5677e+02, 1.9435e+02, 6.0078e-01, 2.4278e-04, 8.8537e-06]])

diffsptk.functional.mgc2sp(mc: Tensor, fft_length: int, alpha: float = 0, gamma: float = 0, norm: bool = False, mul: bool = False, n_fft: int = 512, out_format: str = 'power') → Tensor[source]#

Convert mel-cepstrum to spectrum.

Parameters:

mcTensor [shape=(…, M+1)]: Mel-cepstrum.
fft_lengthint >= 2: The number of FFT bins, \(L\).
alphafloat in (-1, 1): The frequency warping factor, \(\alpha\).
gammafloat in [-1, 1]: The gamma parameter, \(\gamma\).
normbool: If True, the input is assumed to be normalized.
mulbool: If True, the input is assumed to be gamma-multiplied.
n_fftint >> L: The number of FFT bins.
out_format[‘db’, ‘log-magnitude’, ‘magnitude’, ‘power’, ‘cycle’, ‘radian’, ‘degree’, ‘complex’]: The output format.

Returns:

outTensor [shape=(…, L/2+1)]: Spectrum.