window

class diffsptk.Window(in_length: int, out_length: int | None = None, *, window: str | int = 'blackman', norm: str | int = 'power', learnable: bool = False)

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.

learnablebool

Whether to make the window learnable.

forward(x: Tensor) → Tensor

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: Tensor, out_length: int | None = None, *, window: str = 'blackman', norm: str = 'power') → Tensor

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.

See also

frame unframe spec