torch.hann_window#

torch.hann_window(window_length,periodic=True,*,dtype=None,layout=torch.strided,device=None,requires_grad=False)→Tensor#

Hann window function.

$$w[n]=\frac{1}{2}[1-\cos \left(\frac{2\pi n}{N-1}\right)]={\sin }^2\left(\frac{\pi n}{N-1}\right),$$w[n]=21​ [1−cos(N−12πn​)]=sin2(N−1πn​),

where$N$N is the full window size.

The inputwindow_length is a positive integer controlling thereturned window size.periodic flag determines whether the returnedwindow trims off the last duplicate value from the symmetric window and isready to be used as a periodic window with functions liketorch.stft(). Therefore, ifperiodic is true, the$N$N inabove formula is in fact$\text{window\_length}+1$window_length+1. Also, we always havetorch.hann_window(L,periodic=True) equal totorch.hann_window(L+1,periodic=False)[:-1]).

Note

Ifwindow_length$=1$=1, the returned window contains a single value 1.

Parameters

• window_length (int) – the size of returned window

• periodic (bool,optional) – If True, returns a window to be used as periodicfunction. If False, return a symmetric window.

Keyword Arguments

• dtype (torch.dtype, optional) – the desired data type of returned tensor.Default: ifNone, uses a global default (seetorch.set_default_dtype()). Only floating point types are supported.

• layout (torch.layout, optional) – the desired layout of returned window tensor. Onlytorch.strided (dense layout) is supported.

• device (torch.device, optional) – the desired device of returned tensor.Default: ifNone, uses the current device for the default tensor type(seetorch.set_default_device()).device will be the CPUfor CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool,optional) – If autograd should record operations on thereturned tensor. Default:False.

Returns

A 1-D tensor of size$(\text{window\_length},)$(window_length,) containing the window

Return type

Tensor