espnet2.enh.layers.beamformer.get_WPD_filter

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espnet2.enh.layers.beamformer.get_WPD_filter(Phi: Tensor | ComplexTensor, Rf: Tensor | ComplexTensor, reference_vector: Tensor, diagonal_loading: bool = True, diag_eps: float = 1e-07, eps: float = 1e-08) → Tensor | ComplexTensor

Return the WPD vector.

WPD is the Weighted Power minimization Distortionless response convolutional beamformer. As follows:

h = (Rf^-1 @ Phi_{xx}) / tr[(Rf^-1) @ Phi_{xx}] @ u

Reference: : T. Nakatani and K. Kinoshita, “A Unified Convolutional Beamformer for Simultaneous Denoising and Dereverberation,” in IEEE Signal Processing Letters, vol. 26, no. 6, pp. 903-907, June 2019, doi: 10.1109/LSP.2019.2911179. https://ieeexplore.ieee.org/document/8691481

• Parameters:
  • Phi (torch.complex64/ComplexTensor) – (B, F, (btaps+1) * C, (btaps+1) * C) is the PSD of zero-padded speech [x^T(t,f) 0 … 0]^T.
  • Rf (torch.complex64/ComplexTensor) – (B, F, (btaps+1) * C, (btaps+1) * C) is the power normalized spatio-temporal covariance matrix.
  • reference_vector (torch.Tensor) – (B, (btaps+1) * C) is the reference_vector.
  • diagonal_loading (bool) – Whether to add a tiny term to the diagonal of psd_n
  • diag_eps (float)
  • eps (float)
• Returns: (B, F, (btaps + 1) * C)
• Return type: filter_matrix (torch.complex64/ComplexTensor)