Analysis of a prediction error method employing orthonormal basis functions in adaptive feedback cancellation for hearing aids

Abstract

Hearing aids often suffer from acoustic feedback, which limits the achievable amplification and may severely degrade sound quality by creating howling artifacts. A potential method of feedback cancellation comprises predicting the feedback path (FP) using an adaptive filter. However, a large model error, or bias, is introduced due to signal correlations. To reduce the bias in the FP estimate, a prediction-error-method (PEM) has been used, which is based on a closed-loop identification of the FP and the auto-regressive modeling of the desired signal. This approach, which represents the FP using an FIR filter, requires a large number of parameters. Furthermore, in a reverberant environment, even a high order of the FIR filter may be insufficient to fully represent the FP, which reduces the convergence rate and limits the maximum stable gain of the system. In this contribution, we introduce a PEM-based method that utilizes orthonormal basis functions to precisely predict the acoustic FP in hearing aids. Simulation results with measured data show that the proposed method outperforms the standard PEM adaptation algorithm in terms of convergence rate and maximum stable gain.