Abstract:
Active noise control algorithms undergo stability problems in the presence of impulsive noise. This thesis investigates such algorithms with online secondary path modeling for impulsive noise and varying acoustic paths. The thesis presents three methods for active noise control, along with improved online secondary path modeling. Firstly, filtered x recursive least square algorithm is applied for both active noise control and online secondary path modeling. This method gave faster convergence, improved stability, and modeling accuracy as compared to existing ones. The filtered x recursive least square algorithm is not robust for abruptly changing acoustic paths. To overcome this problem another method that uses modified gain filtered x recursive least square algorithm for active noise control is presented. Furthermore, it is observed that modified gain filtered x recursive least square achieves the desired performance with overheads of increased complexity. Thus, a hybrid method is proposed which has less computational complexity than the rest methods with no compromise on active noise control system performance.
