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We present a unified network for voice separation of an unknown number ofspeakers. The proposed approach is composed of several separation headsoptimized together with a speaker classification branch. The separation iscarried out in the time domain, together with parameter sharing between allseparation heads. The classification branch estimates the number of speakerswhile each head is specialized in separating a different number of speakers. Weevaluate the proposed model under both clean and noisy reverberant set-tings.Results suggest that the proposed approach is superior to the baseline model bya significant margin. Additionally, we present a new noisy and reverberantdataset of up to five different speakers speaking simultaneously.

Single channel voice separation for unknown number of speakers under reverberant and noisy settings