An improved CLMS algorithm for feedback cancellation in hearing aids

In LMS algorithm-based feedback estimation, the value of the adaptation step size chosen imposes establishes a compromise between the speed at which the algorithm converges to the feedback-path estimate and the misadjustment between the true and estimated feedback paths at steady state. The combined LMS (CLMS) scheme overcomes this issue, but itself suffers from a sluggish adaptation of the mixture parameter during periods of a rapidly-varying or a stationary feedback path, leading to a degradation in the performance of the feedback canceller. In this work, we propose an acoustic feedback canceller with an improved affine combination of two different-step-size LMS filters, for a bias-less estimation of the acoustic feedback. The new filter-combiner parameter controls the filter combination and ensures at least a minimum adaptation of the mixture parameter for a stationary as well as a varying acoustic environment. We analyse the proposed algorithm for feedback reduction and prove that it performs as well as the element filters or even better in some situations, as compared to the CLMS algorithm. A detailed behaviour analysis of the proposed algorithm is also presented for scenarios of a stationary as well as a time-varying acoustic environment of the user. Simulation results verify the validity of the derived expressions.