espnet2.enh.layers.beamformer.get_gev_vector

source

espnet2.enh.layers.beamformer.get_gev_vector(psd_noise: Tensor | ComplexTensor, psd_speech: Tensor | ComplexTensor, mode='power', reference_vector: int | Tensor = 0, iterations: int = 3, diagonal_loading: bool = True, diag_eps: float = 1e-07, eps: float = 1e-08) → Tensor | ComplexTensor

Return the generalized eigenvalue (GEV) beamformer vector:

psd_speech @ h = lambda * psd_noise @ h

Reference: : Blind acoustic beamforming based on generalized eigenvalue decomposition; E. Warsitz and R. Haeb-Umbach, 2007.

• Parameters:
  • psd_noise (torch.complex64/ComplexTensor) – noise covariance matrix (…, F, C, C)
  • psd_speech (torch.complex64/ComplexTensor) – speech covariance matrix (…, F, C, C)
  • mode (str) – one of (“power”, “evd”) “power”: power method “evd”: eigenvalue decomposition (only for torch builtin complex tensors)
  • reference_vector (torch.Tensor or int) – (…, C) or scalar
  • iterations (int) – number of iterations in power method
  • 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)