An optimized NLMS algorithm for acoustic echo cancellation

In order to improve the overall performance of the normalized least-mean-square (NLMS) algorithm, there is the need to control its main parameters, i.e., the normalized step-size and regularization terms. In this context, the variable step-size and variable regularized versions of the NLMS algorithm are designed to address the conflicting requirement of fast convergence and low misadjustment. In this paper, we propose an optimized NLMS algorithm for acoustic echo cancellation (AEC). This algorithm is based on a joint-optimization on both the normalized step-size and regularization parameters, in the context of a state variable model (similar to Kalman filtering). The simulation results indicate that the proposed algorithm can be a reliable choice for AEC applications, since it achieves fast convergence and tracking, low misadjustment, and double-talk robustness.