# Hann_window

Source:`R/gen-namespace-docs.R`

, `R/gen-namespace-examples.R`

, `R/wrapers.R`

`torch_hann_window.Rd`

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`

.

## 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])`

.