stft#

diffsptk.STFT#: alias of ShortTimeFourierTransform

class diffsptk.ShortTimeFourierTransform(frame_length, frame_period, fft_length, *, center=True, zmean=False, mode='constant', window='blackman', norm='power', eps=1e-09, relative_floor=None, out_format='power')[source]#

This module is a simple cascade of framing, windowing, and spectrum calculation.

Parameters:

frame_lengthint >= 1: The frame length in samples, \(L\).
frame_periodint >= 1: The frame period in samples, \(P\).
fft_lengthint >= L: The number of FFT bins, \(N\).
centerbool: If True, pad the input on both sides so that the frame is centered.
zmeanbool: If True, perform mean subtraction on each frame.
mode[‘constant’, ‘reflect’, ‘replicate’, ‘circular’]: The padding method.
window[‘blackman’, ‘hamming’, ‘hanning’, ‘bartlett’, ‘trapezoidal’, ‘rectangular’, ‘nuttall’]: The window type.
norm[‘none’, ‘power’, ‘magnitude’]: The normalization type of the window.
epsfloat >= 0: A small value added to the power spectrum.
relative_floorfloat < 0 or None: The relative floor of the power spectrum in dB.
out_format[‘db’, ‘log-magnitude’, ‘magnitude’, ‘power’, ‘complex’]: The output format.

forward(x)[source]#

Compute short-time Fourier transform.

Parameters:

xTensor [shape=(…, T)]: The input waveform.

Returns:

outTensor [shape=(…, T/P, N/2+1)]: The output spectrogram.

Examples

>>> x = diffsptk.ramp(1, 3)
>>> x
tensor([1., 2., 3.])
>>> stft = diffsptk.STFT(frame_length=3, frame_period=1, fft_length=8)
>>> y = stft(x)
>>> y
tensor([[1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
        [4.0000, 4.0000, 4.0000, 4.0000, 4.0000],
        [9.0000, 9.0000, 9.0000, 9.0000, 9.0000]])

diffsptk.functional.stft(x, *, frame_length=400, frame_period=80, fft_length=512, center=True, zmean=False, mode='constant', window='blackman', norm='power', eps=1e-09, relative_floor=None, out_format='power')[source]#

Compute short-time Fourier transform.

Parameters:

xTensor [shape=(…, T)]: The input waveform.
frame_lengthint >= 1: The frame length in samples, \(L\).
frame_periodint >= 1: The frame period in samples, \(P\).
fft_lengthint >= L: The number of FFT bins, \(N\).
centerbool: If True, pad the input on both sides so that the frame is centered.
zmeanbool: If True, perform mean subtraction on each frame.
mode[‘constant’, ‘reflect’, ‘replicate’, ‘circular’]: The padding method.
window[‘blackman’, ‘hamming’, ‘hanning’, ‘bartlett’, ‘trapezoidal’, ‘rectangular’, ‘nuttall’]: The window type.
norm[‘none’, ‘power’, ‘magnitude’]: The normalization type of the window.
epsfloat >= 0: A small value added to the power spectrum.
relative_floorfloat < 0 or None: The relative floor of the power spectrum in dB.
out_format[‘db’, ‘log-magnitude’, ‘magnitude’, ‘power’, ‘complex’]: The output format.

Returns:

outTensor [shape=(…, T/P, N/2+1)]: The output spectrogram.

stft#

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