  Speech enhancement in hearing aids is a challenging task since the hardware
limits the number of possible operations and the latency needs to be in the
range of only a few milliseconds. We propose a deep-learning model compatible
with these limitations, which we refer to as Group-Communication Filter-and-Sum
Network (GCFSnet). GCFSnet is a causal multiple-input single output enhancement
model using filter-and-sum processing in the time-frequency domain and a
multi-frame deep post filter. All filters are complex-valued and are estimated
by a deep-learning model using weight-sharing through Group Communication and
quantization-aware training for reducing model size and computational
footprint. For a further increase in performance, a low bit rate binaural link
for delayed binaural features is proposed to use binaural information while
retaining a latency of 2ms. The performance of an oracle binaural LCMV
beamformer in non-low-latency configuration can be matched even by a unilateral
configuration of the GCFSnet in terms of objective metrics.
