NARROWBAND FEEDBACK ACTIVE NOISE CONTROL SYSTEMS WITH SECONDARY PATH MODELING USING GAIN-CONTROLLED ADDITIVE RANDOM NOISE

This paper investigates estimation of the secondary path (SP) during the online operation of the filtered-x least mean square (FxLMS) algorithm-based feedback active noise control (FBANC) systems. The proposed method develops upon a previous work where two adaptive filters were used, one for active noise control (ANC) and the other for secondary path modeling (SPM). The proposed method essentially comprises a similar structure as that of the previous method. The objectives here are to suggest modifications to improve upon the slow convergence of SPM filter and the noise reduction (NR) performance in the previous method. The key idea is to employ a gain-controlled modeling signal (generated from the additive random noise signal) mixed with the cancellation signal. The gain-factor for the modeling signal is adjusted such that a large-level modeling signal is used during the transient state of the ANC system. This improves the converge of the SPM filter. As the ANC system converges, the level of the modeling signal is reduced to achieve good NR performance. Besides controlling the level of the modeling, the gain control parameter is employed in adjusting the various other parameters too, viz. fixed step-size, regularization parameter, convergence monitoring parameter, while computing the time-varying normalized step-size for the SPM filter. The simulation results demonstrate that the proposed method (equipped with the proposed modifications) outperforms the previous method and yet with a negligible increase in the computational complexity