window#

class diffsptk.Window(in_length, out_length=None, *, window='blackman', norm='power')[source]#

See this page for details.

Parameters:

in_lengthint >= 1: The window length, \(L_1\).
out_lengthint >= L1 or None: The output length, \(L_2\). If \(L_2 > L_1\), output is zero-padded. If None, \(L_2 = L_1\).
window[‘blackman’, ‘hamming’, ‘hanning’, ‘bartlett’, ‘trapezoidal’, ‘rectangular’, ‘nuttall’]: The window type.
norm[‘none’, ‘power’, ‘magnitude’]: The normalization type of the window.

forward(x)[source]#

Apply a window function to the given waveform.

Parameters:

xTensor [shape=(…, L1)]: The input framed waveform.

Returns:

outTensor [shape=(…, L2)]: The windowed waveform.

Examples

>>> x = torch.ones(5)
>>> window = diffsptk.Window(5, out_length=7, window="hamming", norm="none")
>>> y = window(x)
>>> y
tensor([0.0800, 0.5400, 1.0000, 0.5400, 0.0800, 0.0000, 0.0000])

diffsptk.functional.window(x, out_length=None, *, window='blackman', norm='power')[source]#

Apply a window function to the given waveform.

Parameters:

xTensor [shape=(…, L1)]: The input framed waveform.
out_lengthint >= L1 or None: The output length, \(L_2\). If \(L_2 > L_1\), output is zero-padded. If None, \(L_2 = L_1\).
window[‘blackman’, ‘hamming’, ‘hanning’, ‘bartlett’, ‘trapezoidal’, ‘rectangular’, ‘nuttall’]: The window type.
norm[‘none’, ‘power’, ‘magnitude’]: The normalization type of the window.

Returns:

outTensor [shape=(…, L2)]: The windowed waveform.

window#

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