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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 length
window_length
and shape parameterbeta
.Let I_0 be the zeroth order modified Bessel function of the first kind (see
torch.i0()
) andN = L - 1
ifperiodic
is False andL
ifperiodic
is True, whereL
is thewindow_length
. This function computes:Calling
torch.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 shorthand to produce a periodic window as input to functions liketorch.stft()
.Note
If
window_length
is one, then the returned window is a single element tensor containing a one.- Parameters
- Keyword Arguments
-
- dtype (
torch.dtype
, optional) – the desired data type of returned tensor. Default: ifNone
, uses a global default (seetorch.set_default_tensor_type()
). - 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_tensor_type()
).device
will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. - requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default:
False
.
- dtype (
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https://pytorch.org/docs/2.1/generated/torch.kaiser_window.html