An Optimized Adaptive Filtering for Speech Noise Cancellation


The maininterest in adaptive filters continues to grow as they begin to find practical applications in areassuch as channel equalization, echo cancellation, noise cancellation and many other adaptive signal-processing applications. The work presented in this paper focuses on optimizingmost popular adaptive filtering algorithms namely Least Mean Square (LMS) algorithm, Normalized Least Mean Square (NLMS) and Recursiveleast Squares (RLS) by using genetic optimizer approach. The tap-length are updated with the three adaptive algorithms according to the value of mean square error based on genetic style. The simulation results for noise cancellation in speechenhancementdemonstrate the good performance of the proposed algorithm in attenuating the noise with less hardware resources complexity.It is a nicetradeoff betweenhardware complexity, SNR ratioand the convergence speed.