Abstract

Acoustic feedback is a persistent problem in hearing aids, which limits the achievable amplification and may severely degrade the sound quality by producing howling artifacts. A potential approach to feedback cancellation is to estimate the feedback path utilizing an adaptive filter. However, estimation of the feedback path suffers a large model error, known as the bias, due to the correlation between the loudspeaker and source signals. A prediction-error method (PEM) based prewhitening filter has been widely utilized to reduce the bias. This approach, however, requires a large number of adaptive parameters, thus increasing the computational complexity, reducing the convergence rate, and limiting the added stable gain. We introduce an adaptive feedback cancellation (AFC) algorithm derived based on the orthonormal basis functions (OBFs) for closed-loop identification of the feedback path by minimizing the prediction error. The OBFs are defined by a set of fixed poles and a small number of adaptive tap-output weights. We study two methods for obtaining the fixed poles, an inherently stable least-squares method and a log-scale frequency resolution method. The poles are then embedded as the a priori information into the algorithm. The proposed algorithm is extensively evaluated with speech and music source signals and with sudden changes in the feedback path. The experimental results show that the proposed method significantly increases the added stable gain, accelerates the convergence rate, and enhances the sound quality compared to state-of-the-art, while requiring far fewer adaptive parameters which leads to reduced computational complexity.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.