espnet2.enh.layers.beamformer.get_mvdr_vector_with_rtf

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espnet2.enh.layers.beamformer.get_mvdr_vector_with_rtf(psd_n: Tensor | ComplexTensor, psd_speech: Tensor | ComplexTensor, psd_noise: Tensor | ComplexTensor, iterations: int = 3, reference_vector: int | Tensor | None = None, diagonal_loading: bool = True, diag_eps: float = 1e-07, eps: float = 1e-08) → Tensor | ComplexTensor

Return the MVDR (Minimum Variance Distortionless Response) vector : calculated with RTF: <br/> h = (Npsd^-1 @ rtf) / (rtf^H @ Npsd^-1 @ rtf)

Reference: : On optimal frequency-domain multichannel linear filtering for noise reduction; M. Souden et al., 2010; https://ieeexplore.ieee.org/document/5089420

• Parameters:
  • psd_n (torch.complex64/ComplexTensor) – observation/noise covariance matrix (…, F, C, C)
  • psd_speech (torch.complex64/ComplexTensor) – speech covariance matrix (…, F, C, C)
  • psd_noise (torch.complex64/ComplexTensor) – noise covariance matrix (…, F, C, C)
  • iterations (int) – number of iterations in power method
  • reference_vector (torch.Tensor or int) – (…, C) or scalar
  • diagonal_loading (bool) – Whether to add a tiny term to the diagonal of psd_n
  • diag_eps (float)
  • eps (float)
• Returns: (…, F, C)
• Return type: beamform_vector (torch.complex64/ComplexTensor)