Comparison of two binaural beamforming approaches for hearing aids

Abstract

Beamforming algorithms in binaural hearing aids are crucial to improve speech understanding in background noise for hearing impaired persons. In this study, we compare and evaluate the performance of two recently proposed minimum variance (MV) beamforming approaches for binaural hearing aids. The binaural linearly constrained MV (BLCMV) beamformer applies linear constraints to maintain the target source and mitigate the interfering sources, taking into account the reverberant nature of sound propagation. The inequality constrained MV (ICMV) beamformer applies inequality constraints to maintain the target source and mitigate the interfering sources, utilizing estimates of the direction of arrivals (DOAs) of the target and interfering sources. The similarities and differences between these two approaches is discussed and the performance of both algorithms is evaluated using simulated data and using real-world recordings, particularly focusing on the robustness to estimation errors of the relative transfer functions (RTFs) and DOAs. The BLCMV achieves a good performance if the RTFs are accurately estimated while the ICMV shows a good robustness to DOA estimation errors.