Acoustic Echo Cancellation using Conventional Adaptive Algorithms and Modified Variable Step Size LMS Algorithm

Abstract

Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including echo cancellation, adaptive equalization, adaptive noise cancellation, and adaptive beamforming. Acoustic echo cancellation is a common occurrence in todays telecommunication systems. The signal interference caused by acoustic echo is distracting to users and causes a reduction in the quality of the communication. Echo cancellers are very successful and today almost no echo at all can be perceived while using telephones A variable step size least mean square algorithm (VSS LMS) is given with significant changes in which scalar step size increases or decreases as the squared error increases or decreases, thereby allowing the adaptive filter to track changes in the system and produces a smaller steady state error. A new VSS LMS algorithm is proposed, which can effectively adjust the step size while maintaining the immunity against independent noise disturbance. The modified variable step size LMS (MVSS LMS) algorithm allows more flexible control of misadjustment and convergence time without the need to compromise one for the other. The present work focuses on the conventional adaptive algorithms and modified variable step size LMS algorithm to reduce the unwanted echo, thus improving communication quality. Simulation results are presented to support the analysis and to compare the performance of the modified algorithm with the other conventional adaptive algorithms. They show that the MVSS algorithm provides faster speed of convergence among other variable step size algorithms while retaining the same small level of misadjustment and the mean square error is almost similar to that of the (fixed step size least mean square) FSS-LMS algorithm under similar conditions. An attempt has been made to examine adaptive filtering techniques as they apply to acoustic echo cancellation, to simulate these adaptive filtering algorithms using MATLAB and to compare the performance of these adaptive filtering algorithms as they are applied to the acoustic echo cancellation application.