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arXiv · 2307.08858

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

Nils L. Westhausen·Bernd T. Meyer
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Abstract

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.

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Nils L. Westhausen, Bernd T. Meyer. 2023-07-17. Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids. https://arxiv.org/abs/2307.08858

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