Low-Complexity Individualized Noise Reduction for Real-Time Processing

Recent advancements in deep neural network (DNN)-based hearing aids (HAs) have significantly improved hearing-restoration performance. A recent study introduced a DNN-based framework for designing HA models tailored to the sensorineural hearing loss (SNHL) profile of individual users, improving treatment outcomes for diverse HA users. While these bio-inspired HA models perform well in clean speech conditions, their effectiveness diminishes in noisy environments. Moreover, their high computational complexity makes deployment on resource-constrained devices challenging, particularly when additional noise reduction modules are added. To address these limitations, we propose a low-complexity, real-time individualized noise reduction (INR) model that jointly performs noise suppression and hearing loss compensation. The model is optimized for deployment on embedded systems using quantization to reduce model size and computational load. To mitigate the impact of quantization error, we applied quantization-aware training (QAT), which simulates the quantization effect during the training phase. Experimental results show that the proposed INR model outperforms existing closed-loop bio-inspired HA models in noisy conditions, achieving superior objective measures of speech intelligibility and sound quality. The INT8 quantized version maintains performance comparable to the full-precision model. With a system latency of just 8 ms, the model meets the real-time requirements of hearing aid applications. These findings demonstrate the potential of the proposed INR model for real-time application in hearing aids and other low-power hearing devices.