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Speech enhancement in hearing aids is a challenging task since the hardwarelimits the number of possible operations and the latency needs to be in therange of only a few milliseconds. We propose a deep-learning model compatiblewith these limitations, which we refer to as Group-Communication Filter-and-SumNetwork (GCFSnet). GCFSnet is a causal multiple-input single output enhancementmodel using filter-and-sum processing in the time-frequency domain and amulti-frame deep post filter. All filters are complex-valued and are estimatedby a deep-learning model using weight-sharing through Group Communication andquantization-aware training for reducing model size and computationalfootprint. For a further increase in performance, a low bit rate binaural linkfor delayed binaural features is proposed to use binaural information whileretaining a latency of 2ms. The performance of an oracle binaural LCMVbeamformer in non-low-latency configuration can be matched even by a unilateralconfiguration of the GCFSnet in terms of objective metrics.

Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids