Curvelet based robust improved sine adaptive filter for feedback cancellation in hearing aids

Abstract

Acoustic feedback is a common and persistent issue in hearing aids, which can lead to limitations in the achievable amplification and significant degradation in sound quality, including howling artefacts. Recently, the maximum versoria criteria (MVC) algorithm has been developed to reduce the acoustic feedback in hearing aids in the presence of impulsive noise. However, the robust technique MVC has not addressed the feedback signal’s sparseness characteristic while developing an adaptive feedback canceller. This paper proposes a robust and sparsity-aware technique called curvelet-improved sine-based adaptive filtering (CISAF) algorithm for adaptive feedback cancellation (AFC) in hearing aids using the prediction error method (PEM) in the case of non-gaussian interference. The step factor is enhanced based on the logarithmic cosh function to speed up the algorithm’s convergence while guaranteeing a low steady-state error. This variable step size (VSS) is included with CISAF as CISAF-VSS technique. Computer simulations show that the suggested method achieves a good convergence rate, maintains high speech quality, keeps a high segmental SNR (segSNR) and short-time objective intelligibility (STOI) at steady-state.