DeepPEM-AFC: An Improved Prediction-Error-Method-based Adaptive Feedback Cancellation with Deep Learning for Hearing Aids

Xiaofan Zhan, Fengyuan Hao, Xiaodong Li, Chengshi Zheng

Abstract: Hearing assistive devices aim to compensate hearing loss for hearing-impaired listeners, and their maximum stable gain (MSG) is constrained because of the existence of the acoustic feedback between the receiver and microphone, resulting in their inefficiency for individuals with severe or profound hearing loss who require very large amplification gain. Adaptive feedback cancellation (AFC) is an effective method to reduce acoustic feedback and increase MSG but its performance often degrades because of the high correlation between the target and feedback signals. The prediction-error-method (PEM)-based AFC has shown its capability in reducing this degradation. This paper proposes a deep learning-based PEM-AFC dubbed DeepPEM-AFC to further improve the performance of traditional PEM-AFC by fully taking advantage of both deep learning in automatically finding the optimal step size when updating the filter coefficients and PEM in solving the abovementioned high-correlation problem. To improve generalization across different acoustic feedback paths, a path generation scheme is proposed for training purposes. Experimental results show that DeepPEM-AFC achieves superior tracking performance compared to state-of-the-art methods, including traditional methods and Neural-AFC. Moreover, combining DeepPEM-AFC with frequency shifting further improves the performance.