torch.hamming_window

torch.hamming_window(window_length, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False) → Tensor

Hamming window function.

$$w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),$$

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 $\text{window\_length} + 1$. Also, we always have torch.hamming_window(L, periodic=True) equal to torch.hamming_window(L + 1, periodic=False)[:-1]).

Note

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

Note

This is a generalized version of torch.hann_window().

Parameters

window_length (int) – the size of returned 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_dtype()). 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 None, uses the current device for the default tensor type (see torch.set_default_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

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

Returns

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

Return type

Tensor

torch.hamming_window(window_length, periodic, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False) → Tensor

Hamming window function with periodic specified.

Parameters

• window_length (int) – the size of returned window

• periodic (bool) – If True, returns a window to be used as periodic function. If False, return a symmetric 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_dtype()). 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 None, uses the current device for the default tensor type (see torch.set_default_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

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

Returns

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

Return type

Tensor

torch.hamming_window(window_length, periodic, float alpha, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False) → Tensor

Hamming window function with periodic and alpha specified.

Parameters

• window_length (int) – the size of returned window

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

• alpha (float) – The coefficient $\alpha$ in the equation above

Keyword Arguments

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_dtype()). 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 None, uses the current device for the default tensor type (see torch.set_default_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

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

Returns

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

Return type

Tensor

torch.hamming_window(window_length, periodic, float alpha, float beta, *, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False) → Tensor

Hamming window function with periodic, alpha and beta specified.

Parameters

• window_length (int) – the size of returned window

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

• alpha (float) – The coefficient $\alpha$ in the equation above

• beta (float) – The coefficient $\beta$ in the equation above

Keyword Arguments

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_dtype()). 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 None, uses the current device for the default tensor type (see torch.set_default_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

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

Returns

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

Return type

Tensor