Audiogram-Informed End-to-End Noise Reduction and Wide Dynamic Range Compression for Hearing Aids

Wide dynamic range compression (WDRC) provides level-dependent amplification, intended to make the output of a hearing aid fall between the hearing threshold and the highest comfortable level of the listener. Hearing aids often combine noise reduction with WDRC, applied sequentially. Unfortunately, this can result in across-source modulation, especially when fast-acting compression is used. However, fast-acting compression is theoretically preferable to compensate for the loss of compression in the cochlea. To apply fast-acting compression to speech while avoiding across-source modulation, we propose a deep-learning-based method that integrates noise reduction and source-independent WDRC in a two-stage low-complexity framework. The method applies fast-acting compression to speech and slow-acting compression to noise by an amount depending on the audiogram, with a controllable residual noise level. Objective measurements using simulated hearing-impaired listeners showed that the proposed method reduced negative interactions of speech and noise in highly non-stationary noise scenarios and generalized well across various hearing losses.