b2mc

class diffsptk.MLSADigitalFilterCoefficientsToMelCepstrum(cep_order, alpha=0)

See this page for details.

Parameters:

cep_orderint >= 0

Order of cepstrum, \(M\).

alphafloat in (-1, 1)

Frequency warping factor, \(\alpha\).

References

[1]

K. Tokuda et al., “Spectral estimation of speech by mel-generalized cepstral analysis,” Electronics and Communications in Japan, part 3, vol. 76, no. 2, pp. 30-43, 1993.

forward(b)

Convert MLSA filter coefficients to mel-cepstrum.

Parameters:

bTensor [shape=(…, M+1)]

MLSA filter coefficients.

Returns:

outTensor [shape=(…, M+1)]

Mel-cepstral coefficients.

Examples

>>> b = diffsptk.ramp(4)
>>> mc2b = diffsptk.MelCepstrumToMLSADigitalFilterCoefficients(4, 0.3)
>>> b2mc = diffsptk.MLSADigitalFilterCoefficientsToMelCepstrum(4, 0.3)
>>> b2 = mc2b(b2mc(b))
>>> b2
tensor([0.0000, 1.0000, 2.0000, 3.0000, 4.0000])

diffsptk.functional.b2mc(b, alpha=0)

Convert MLSA filter coefficients to mel-cepstrum.

Parameters:

bTensor [shape=(…, M+1)]

MLSA filter coefficients.

alphafloat in (-1, 1)

Frequency warping factor, \(\alpha\).

Returns:

outTensor [shape=(…, M+1)]

Mel-cepstral coefficients.

See also

mc2b