smcep#

class diffsptk.SecondOrderAllPassMelCepstralAnalysis(cep_order, fft_length, alpha=0, theta=0, n_iter=0, accuracy_factor=4)[source]#

See this page for details. Note that the current implementation does not use the efficient Toeplitz-plus-Hankel system solver.

Parameters:

cep_orderint >= 0: Order of mel-cepstrum, \(M\).
fft_lengthint >= 2M: Number of FFT bins, \(L\).
alphafloat in (-1, 1): Frequency warping factor, \(\alpha\).
thetafloat in [0, 1]: Emphasis frequency, \(\theta\).
n_iterint >= 0: Number of iterations.
accuracy_factorint >= 1: Accuracy factor multiplied by FFT length.

References

[1]

T. Wakako et al., “Speech spectral estimation based on expansion of log spectrum by arbitrary basis functions,” IEICE Trans, vol. J82-D-II, no. 12, pp. 2203-2211, 1999 (in Japanese).

forward(x)[source]#

Estimate mel-cepstrum from spectrum.

Parameters:

xTensor [shape=(…, L/2+1)]: Power spectrum.

Returns:

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

Examples

>>> x = diffsptk.ramp(19)
>>> stft = diffsptk.STFT(frame_length=10, frame_period=10, fft_length=16)
>>> smcep = diffsptk.SecondOrderAllPassMelCepstralAnalysis(3, 16, 0.1, n_iter=1)
>>> mc = smcep(stft(x))
>>> mc
tensor([[-0.8851,  0.7917, -0.1737,  0.0175],
        [-0.3523,  4.4223, -1.0883, -0.0510]])

smcep#

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