  In this paper, we introduce a causal low-latency low-complexity approach for
binaural 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 the
time-frequency domain.
  We apply Group Communication (GC),
  i.e., latent model variables are split into groups and processed with a
shared sequence model with the aim of reducing the complexity of a simple model
only containing one convolutional and one recurrent module.
  With GC we are able to reduce the size of the model by up to 83 % and the
complexity up to 73 % compared to the model without GC, while mostly retaining
performance.
  Even for the smallest model configuration, GCBFSnet matches the performance
of a low-complexity TasNet baseline in most metrics despite the larger size and
higher number of required operations of the baseline.
