Hann_window

Usage

torch_hann_window(
  window_length,
  periodic = TRUE,
  dtype = NULL,
  layout = NULL,
  device = NULL,
  requires_grad = FALSE
)

Arguments

window_length: (int) the size of returned window
periodic: (bool, optional) If TRUE, returns a window to be used as periodic function. If False, return a symmetric 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). Only floating point types are supported.
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 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` \eqn{=1}, the returned window contains a single value 1.

hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor

Hann window function.

$$ w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] = \sin^2 \left( \frac{\pi n}{N - 1} \right), $$ where $N$ is the full window size.

The input window_length is a positive integer controlling the returned window size. periodic flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like torch_stft. Therefore, if periodic is true, the $N$ in above formula is in fact $\mbox{window\_length} + 1$. Also, we always have torch_hann_window(L, periodic=TRUE) equal to torch_hann_window(L + 1, periodic=False)[:-1]).