A speech enhancement method combining beamforming with RNN for hearing aids

Abstract

Speech enhancement is essential for hearing aids. In recent years, many speech enhancement methods based on deep learning have been proven to be effective. However, these speech enhancement methods rarely consider limited hardware resources and have difficulty meeting real-time requirements, which is very important for hearing aids. To solve the above problems, we propose a method that combines beamforming and speech enhancement methods based on deep learning. Beamforming is used to filter background noise and reduce the complexity of noise. Additionally, a new filter bank used in hearing aids is adopted to reduce the complexity of the system. The system was deployed and tested in resource-constrained hearing aids. The effectiveness of the method was verified by objective experiments using standard evaluation indicators. The results showed that the power was 8.43 mA, the signal-to-noise ratio improved by 9.4394 dB, and the PESQ improved by 0.7350. The presented objective and subjective results show that the proposed method achieves better noise suppression than previous methods.