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In this paper, we introduce a causal low-latency low-complexity approach forbinaural multichannel blind speaker separation in noisy reverberant conditions.  The model, referred to as Group Communication Binaural Filter and Sum Network(GCBFSnet) predicts complex filters for filter-and-sum beamforming in thetime-frequency domain.  We apply Group Communication (GC),  i.e., latent model variables are split into groups and processed with ashared sequence model with the aim of reducing the complexity of a simple modelonly containing one convolutional and one recurrent module.  With GC we are able to reduce the size of the model by up to 83 % and thecomplexity up to 73 % compared to the model without GC, while mostly retainingperformance.  Even for the smallest model configuration, GCBFSnet matches the performanceof a low-complexity TasNet baseline in most metrics despite the larger size andhigher number of required operations of the baseline.

Binaural multichannel blind speaker separation with a causal low-latency and low-complexity approach