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.
dtype: (torch.dtype, optional) the desired data type of returned tensor. Default: if NULL, uses a global default (see torch_set_default_tensor_type). If dtype is not given, infer the data type from the other input arguments. If any of start, end, or stop are floating-point, the dtype is inferred to be the default dtype, see ~torch.get_default_dtype. Otherwise, the dtype is inferred to be torch.int64.
layout: (torch.layout, optional) the desired layout of returned Tensor. Default: torch_strided.
device: (torch.device, optional) the desired device of returned tensor. Default: if NULL, uses the current device for the default tensor type (see torch_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.
Note
If window_length is one, then the returned window is a single element tensor containing a one.
Computes the Kaiser window with window length window_length and shape parameter beta.
Let I_0 be the zeroth order modified Bessel function of the first kind (see torch_i0()) and N = L - 1 if periodic is FALSE and L if periodic is TRUE, where L is the window_length. This function computes:
outi=I0β1−(N/2i−N/2)2/I0(β)
Calling torch_kaiser_window(L, B, periodic=TRUE) is equivalent to calling torch_kaiser_window(L + 1, B, periodic=FALSE)[:-1]). The periodic argument is intended as a helpful shorthand to produce a periodic window as input to functions like torch_stft().