torch.hamming_window#

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

Hamming window function.

$$w[n]=\alpha -\beta \cos \left(\frac{2\pi n}{N-1}\right),$$w[n]=α−β cos(N−12πn​),

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.hamming_window(L,periodic=True) equal totorch.hamming_window(L+1,periodic=False)[:-1]).

Note

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

Note

This is a generalized version oftorch.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: 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.

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

• 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

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 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.

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

• 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

torch.hamming_window(window_length,periodic,floatalpha,*,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 periodicfunction. If False, return a symmetric window.

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

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.

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

• 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

torch.hamming_window(window_length,periodic,floatalpha,floatbeta,*,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 periodicfunction. If False, return a symmetric window.

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

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

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.

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

• 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