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torch

torch_kaiser_window

Kaiser_window

Description

Kaiser_window

Usage

torch_kaiser_window(
  window_length,
  periodic,
  beta,
  dtype = torch_float(),
  layout = NULL,
  device = NULL,
  requires_grad = NULL
)

Arguments

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.
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.

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 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:

out_i = I_0 ≤ft( β √{1 - ≤ft( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( 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().

Note

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

torch

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0
MIT + file LICENSE
Authors
Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]
Initial release

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