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This paper describes frequency-domain nonlinear mixture of beamformers that can extract a speech source from a known direction when there are fewer microphones than sources (the underdetermined case). Our approach models the data in each frequency bin via Gaussian mixture distributions, which can be learned using the expectation maximization algorithm. The model learning is performed using the observed...
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