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We present a new method for separating a mixed audio sequence, in whichmultiple voices speak simultaneously. The new method employs gated neuralnetworks that are trained to separate the voices at multiple processing steps,while maintaining the speaker in each output channel fixed. A different modelis trained for every number of possible speakers, and the model with thelargest number of speakers is employed to select the actual number of speakersin a given sample. Our method greatly outperforms the current state of the art,which, as we show, is not competitive for more than two speakers.

Voice Separation with an Unknown Number of Multiple Speakers