Selective Fixed-filter Active Noise Control Based on Parallel Structure and Bayesian Tracking

Active noise control (ANC) is an effective method for suppressing unwanted noise. While most commercial ANC products rely on pre-trained fixed-filters for noise attenuation due to their high computational efficiency and stability, the noise reduction (NR) performance is prone to variations in the direction of incident noise and noise spectral content mismatch. In this paper, a selective fixed-filter ANC algorithm based on parallel structure and Bayesian tracking (SFANC-PSBT) is proposed to mitigate these problems. Bayesian tracking is applied to deal with the noise source direction uncertainties and to select the grid-based fixed filter with the largest posterior probability. Moreover, the parallel broadband fixed-filter adaptive gain (PBFAG) approach is proposed to improve NR performance for off-grid directions, and the parallel narrowband fixed-filter adaptive gain (PNFAG) approach is proposed to further attenuate noise and to mitigate the noise spectral content mismatch issue. Besides, stochastic analysis of the proposed methods is provided. Experimental results demonstrate that the proposed methods achieve better NR performance, faster convergence rate and higher robustness against noise variations under changing primary path conditions. The PNFAG and PBFAG methods yield NR improvements of 23.71% and 18.67%, respectively, over the conventional fixed-filter method. Some before/after ANC sound samples under different noise conditions can be found in https://zhchengcq.github.io/ANCdemo/.