class_docstr: Generalized EigenValue decomposition (GEV) Beamforming.
  
  Example
  -------
  >>> from speechbrain.dataio.dataio import read_audio
  >>> import torch
  >>>
  >>> from speechbrain.processing.features import STFT, ISTFT
  >>> from speechbrain.processing.multi_mic import Covariance
  >>> from speechbrain.processing.multi_mic import Gev
  >>>
  >>> xs_speech = read_audio(
  ...    'tests/samples/multi-mic/speech_-0.82918_0.55279_-0.082918.flac'
  ... )
  >>> xs_speech  = xs_speech.unsqueeze(0) # [batch, time, channels]
  >>> xs_noise = read_audio('tests/samples/multi-mic/noise_0.70225_-0.70225_0.11704.flac')
  >>> xs_noise = xs_noise.unsqueeze(0)
  >>> fs = 16000
  >>> ss = xs_speech
  >>> nn = 0.05 * xs_noise
  >>> xs = ss + nn
  >>>
  >>> stft = STFT(sample_rate=fs)
  >>> cov = Covariance()
  >>> gev = Gev()
  >>> istft = ISTFT(sample_rate=fs)
  >>>
  >>> Ss = stft(ss)
  >>> Nn = stft(nn)
  >>> Xs = stft(xs)
  >>>
  >>> SSs = cov(Ss)
  >>> NNs = cov(Nn)
  >>>
  >>> Ys = gev(Xs, SSs, NNs)
  >>> ys = istft(Ys)
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code: def __init__(self):
      super().__init__()
  
imports: import torch
  from packaging import version
  import speechbrain.processing.decomposition
label_desc: Use image node_id 1 to create a new Gev object from inherited base classes: torch.nn with example: obj = Gev()