Scheduled-Stepsize NLMS Algorithm

This paper presents a method of scheduling stepsizes for the normalized least-mean-squares (SS-NLMS) algorithm. Geometrically interpreting the mean square deviation (MSD) learning curve leads to establishing an objective curve and to constructing a lookup table of stepsizes in order for the MSD to follow the curve. The SS-NLMS shows not only good performance but also robustness with respect to different signal-to-noise ratio (SNR) in measurement noise and different correlation in input signals with a very small number of online computations. Moreover, the scalability of the tabled stepsize with respect to the number of taps is described. For the efficient memory usage in practice, a modified version replaces the tabled stepsizes by down-sampled stepsizes with no performance degradation.X1122sciescopu

Two-stage active noise control with online secondary-path filter based on an adapted scheduled-stepsize NLMS algorithm

A two-stage active noise control (ANC) system is proposed for non-stationary environments: a secondary-path filtering (SPF) stage and a control filtering (CF) stage. The secondary-path filter is roughly trained as quickly as possible in the SPF stage. Based on the trained secondary-path filter, the control filter is trained to minimize the residual errors sensed by an error microphone in the CF stage. A stage-switching algorithm is designed to exchange between the SPF stage and the CF stage based only on signals from the error microphone, which moves the CF stage to the SPF stage whenever the residual errors reach up to a certain level in which the control filter cannot suppress the residual errors mainly caused by the change of the secondary path. To train the secondary-path filter and the control filter quickly and robustly, a scheduled-stepsize normalized least mean square (NLMS) algorithm is adapted to handle not only measurement noises but also disturbances mutually generated between the training of the secondary-path filter and that of the control filter. Since the adapted scheduled-stepsize NLMS algorithm presets the optimal stepsizes for each iteration, the proposed ANC system trains quickly the filters without the additional computations and reduces the residual errors over other ANC systems. (C) 2019 Elsevier Ltd. All rights reserved.11Nsciescopu