spec#

class diffsptk.Spectrum(fft_length, *, eps=0, relative_floor=None, out_format='power')[source]#

See this page for details.

Parameters:

fft_lengthint >= 2: Number of FFT bins, \(L\).
epsfloat >= 0: A small value added to power spectrum.
relative_floorfloat < 0 or None: Relative floor in decibels.
out_format[‘db’, ‘log-magnitude’, ‘magnitude’, ‘power’]: Output format.

forward(b=None, a=None)[source]#

Compute spectrum.

Parameters:

bTensor [shape=(…, M+1)] or None: Framed waveform or numerator coefficients.
aTensor [shape=(…, N+1)] or None: Denominator coefficients.

Returns:

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

Examples

>>> x = diffsptk.ramp(1, 3)
>>> x
tensor([1., 2., 3.])
>>> spec = diffsptk.Spectrum(8)
>>> y = spec(x)
>>> y
tensor([36.0000, 25.3137,  8.0000,  2.6863,  4.0000])

diffsptk.functional.spec(b=None, a=None, *, fft_length=512, eps=0, relative_floor=None, out_format='power')[source]#

Compute spectrum.

Parameters:

bTensor [shape=(…, M+1)] or None: Numerator coefficients.
aTensor [shape=(…, N+1)] or None: Denominator coefficients.
fft_lengthint >= 2: Number of FFT bins, \(L\).
epsfloat >= 0: A small value added to power spectrum.
relative_floorfloat < 0 or None: Relative floor in decibels.
out_format[‘db’, ‘log-magnitude’, ‘magnitude’, ‘power’]: Output format.

Returns:

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