Deep Feedback Cancellation for Hearing Aids with Improved System Stability and Sound Quality

Abstract

Acoustic feedback cancellation is an important task in audio processing systems, aiming to mitigate the effects of feedback loops on system stability and sound quality. State-of-the-art methods rely on adaptive filtering algorithms and face challenges in balancing between rapid convergence and low steady-state error. In this work, we introduce a novel approach inspired by traditional adaptive filtering and deep learning techniques to achieve a significantly faster convergence and lower steady-state errors at the same time. Our proposed system, termed Deep Feedback Cancellation (DFC), leverages deep neural networks to predict the impulse response of the feedback path directly. Hence, it replaces traditional gradient based adaptive estimation of the impulse responses. Experimental evaluation, in a hearing aid setting, conducted on real-world data demonstrates the superiority of DFC over traditional methods. Specifically, in a practically very important situation, where the feedback path undergoes rapid changes, the proposed DFC achieves increased convergence rate by a factor of 30, while decreasing the steady-state error by 2 dB. Generally, DFC leads to very significant improvements over traditional methods. These improvements are confirmed by objective evaluations and subjective listening tests. Our findings suggest that DFC presents a promising alternative for acoustic feedback cancellation in hearing aid applications.