LAUSR

Abstract 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.