torch.signal.windows.hamming

torch.signal.windows.hamming(M, *, sym=True, dtype=None, layout=torch.strided, device=None, requires_grad=False)

Computes the Hamming window.

The Hamming window is defined as follows:

$$w_n = \alpha - \beta\ \cos \left( \frac{2 \pi n}{M - 1} \right)$$

The window is normalized to 1 (maximum value is 1). However, the 1 doesn’t appear if M is even and sym is True.

Parameters

M (int) – the length of the window. In other words, the number of points of the returned window.

Keyword Arguments

• sym (bool, optional) – If False, returns a periodic window suitable for use in spectral analysis. If True, returns a symmetric window suitable for use in filter design. Default: True.

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

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

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_dtype()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• 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.

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

Return type

Tensor

Examples:

>>> # Generates a symmetric Hamming window.
>>> torch.signal.windows.hamming(10)
tensor([0.0800, 0.1876, 0.4601, 0.7700, 0.9723, 0.9723, 0.7700, 0.4601, 0.1876, 0.0800])

>>> # Generates a periodic Hamming window.
>>> torch.signal.windows.hamming(10, sym=False)
tensor([0.0800, 0.1679, 0.3979, 0.6821, 0.9121, 1.0000, 0.9121, 0.6821, 0.3979, 0.1679])