A Novel Technique for an Adaptive Feedback Canceller for Hearing Aids

Abstract

This chapter is focused on the implementation aspects of adaptive feedback canceller algorithms and their computational complexity when reducing misalignment and convergence rates. When an adaptive algorithm filter was used for modeling the acoustic feedback, there was wide misalignment due to a fixed step size. Through the use of the prediction–error method (PEM), the bias in the algorithm for an adaptive filter was reduced. The PEM used a variable step size and a full range of adaptive filters were used as a trade-off between the misalignment and the convergence speed. Various performance measures were considered in order to study these algorithms: misalignment, maximum stable gain, added stable gain, and the algorithm execution time. The disadvantage of misalignment and convergence when changing step size has been addressed using a new algorithm that has automatic step size adjustment. This new algorithm demonstrated effectiveness in controlling misalignment. The findings reveal that the misalignment, maximum added gain, and added stable gain improved with the use of the new adaptive filter algorithm. Despite this, the PEM did not satisfy user requirements and so a new system named AFC-PEM MPVSS is proposed. Furthermore, work has been done to measure the quality of signal.