torch / torch
torch.hann_window¶
-
torch.
hann_window
(window_length, periodic=True, *, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor¶ Hann window function.
where 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 liketorch.stft()
. Therefore, ifperiodic
is true, the in above formula is in fact . Also, we always havetorch.hann_window(L, periodic=True)
equal totorch.hann_window(L + 1, periodic=False)[:-1])
.Note
If
window_length
, the returned window contains a single value 1.- Parameters
- Keyword Arguments
dtype (
torch.dtype
, optional) – the desired data type of returned tensor. Default: ifNone
, uses a global default (seetorch.set_default_tensor_type()
). 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_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
.
- Returns
A 1-D tensor of size containing the window
- Return type
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