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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 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:

  o u t i=I 0(β 1−(i−N/2 N/2)2)/I 0(β)out_i = I_0 \left( \beta \sqrt{1 - \left( {\frac{i - N/2}{N/2}} \right) ^2 } \right) / I_0( \beta ) o u t i​=I 0​
  ​β 1−(N/2 i−N/2​)2
  ​
  ​/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.

  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: if `None`, uses a global default (see `torch.set_default_tensor_type()`).

    - layout (`torch.layout`, optional) – the desired layout of returned window tensor. Only `torch.strided` (dense layout) is supported.

    - device (`torch.device`, optional) – the desired device of returned tensor. Default: if `None`, 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`.

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