torch.kaiser_window#

torch.kaiser_window(window_length,periodic=True,beta=12.0,*,dtype=None,layout=torch.strided,device=None,requires_grad=False)→Tensor#

Computes the Kaiser window with window lengthwindow_length and shape parameterbeta.

Let I_0 be the zeroth order modified Bessel function of the first kind (seetorch.i0()) andN=L-1 ifperiodic is False andL ifperiodic is True,whereL is thewindow_length. This function computes:

$$ou{t}_i=I_0\left(\beta \sqrt{1-{\left(\frac{i-N\mathrm{/}2}{N\mathrm{/}2}\right)}^2}\right)\mathrm{/}{I}_0(\beta )$$outi​=I0​​β1−(N/2i−N/2​)2​​/I0​(β)

Callingtorch.kaiser_window(L,B,periodic=True) is equivalent to callingtorch.kaiser_window(L+1,B,periodic=False)[:-1]).Theperiodic argument is intended as a helpful shorthandto produce a periodic window as input to functions liketorch.stft().

Note

Ifwindow_length is one, then the returned window is a single element tensor containing a one.

Parameters

• window_length (int) – length of the window.

• periodic (bool,optional) – If True, returns a periodic window suitable for use in spectral analysis.If False, returns a symmetric window suitable for use in filter design.

• beta (float,optional) – shape parameter for the window.

Keyword Arguments

• dtype (torch.dtype, optional) – the desired data type of returned tensor.Default: ifNone, uses a global default (seetorch.set_default_dtype()).

• layout (torch.layout, optional) – the desired layout of returned window tensor. Onlytorch.strided (dense layout) is supported.

• device (torch.device, optional) – the desired device of returned tensor.Default: ifNone, uses the current device for the default tensor type(seetorch.set_default_device()).device will be the CPUfor CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool,optional) – If autograd should record operations on thereturned tensor. Default:False.