Shortcuts, *, sym=True, dtype=None, layout=torch.strided, device=None, requires_grad=False)[source]

Computes the minimum 4-term Blackman-Harris window according to Nuttall.

wn=10.36358cos(zn)+0.48917cos(2zn)0.13659cos(3zn)+0.01064cos(4zn)w_n = 1 - 0.36358 \cos{(z_n)} + 0.48917 \cos{(2z_n)} - 0.13659 \cos{(3z_n)} + 0.01064 \cos{(4z_n)}

where z_n = 2 π n/ M.

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


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.

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

  • 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_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.

Return type



- A. Nuttall, “Some windows with very good sidelobe behavior,”
  IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 29, no. 1, pp. 84-91,
  Feb 1981.

- Heinzel G. et al., “Spectrum and spectral density estimation by the Discrete Fourier transform (DFT),
  including a comprehensive list of window functions and some new flat-top windows”,
  February 15, 2002


>>> # Generates a symmetric Nutall window.
>>>, sym=True)
tensor([3.6280e-04, 2.2698e-01, 1.0000e+00, 2.2698e-01, 3.6280e-04])

>>> # Generates a periodic Nuttall window.
>>>, sym=False)
tensor([3.6280e-04, 1.1052e-01, 7.9826e-01, 7.9826e-01, 1.1052e-01])


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources