An optimized NLMS algorithm for acoustic echo cancellation

Abstract

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 stepsize and regularization terms. In this context, the variable stepsize 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.

Fast and precise underwater transducer characterisation utilising adaptive system identification

“…Furthermore, it does not include influences of the remaining impulse responses present in the form of reverberation of measurement tanks. A summary of the NPVSS‐NLMS and the JO‐NLMS is provided in the publication [19]. In it, the derivations and the results of the different algorithms for applying acoustic echo cancellation are compared.…”

“…In the simplest case, this is controlled by a fixed step size that determines the adaptation speed and the so-called steady-state performance (that determines the final quality of the estimation). However, a variable step size can often achieve much better performance with respect to both of these quantities [16][17][18][19][20]. This publication presents a novel method for determining an adaptive step size control in an optimal manner, which uses the theoretical behaviour of timeinvariant systems.…”

Intelligent resource allocation scheme for the cognitive radio network in the presence of primary user emulation attack

“…We define Z as a sequence of dimension at i th iteration as If is a sequence which is given by Then following (32a)–(32c) and (33)–(35), we have where is the corresponding k th value of . If the proposed method is solved by using the NLMS algorithm, then the desired output of the system is given by [24] …”