Real-time multichannel deep speech enhancement in hearing aids: Comparing monaural and binaural processing in complex acoustic scenarios

Abstract

Deep learning has the potential to enhance speech signals and increase their intelligibility for users of hearing aids. Deep models suited for real-world application should feature a low computational complexity and low processing delay of only a few milliseconds. In this paper, we explore deep speech enhancement that matches these requirements and contrast monaural and binaural processing algorithms in two complex acoustic scenes. Both algorithms are evaluated with objective metrics and in experiments with hearing-impaired listeners performing a speech-in-noise test. Results are compared to two traditional enhancement strategies, i.e., adaptive differential microphone processing and binaural beamforming. While in diffuse noise, all algorithms perform similarly, the binaural deep learning approach performs best in the presence of spatial interferers. Through a post-analysis, this can be attributed to improvements at low SNRs and to precise spatial filtering.

This work is published in IEEE/ACM TASLP. This version corresponds to the accepted version

References in corpus (4)

• Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA)
• Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters
• Deep Multi-Frame MVDR Filtering for Binaural Noise Reduction
• Exploiting spatial information with the informed complex-valued spatial autoencoder for target speaker extraction