Co-Forecasting of Time-Varying Spatial-Frequency Map for Selective Fixed-Filter Multichannel ANC Based on Dynamic Factor Graph

Selective fixed-filter active noise control (SFANC) has emerged as a prominent and practical solution for noise suppression due to its fast response, high computational efficiency, and stability. However, its noise reduction performance still degrades when dealing with some dynamic noise scenarios where the filter switching cannot catch up with the variation of the primary noise source. To tackle this challenge, we proposed a novel approach to employ the dynamic factor graph (DFG) in predicting the direction and frequency bands of primary noise. Once the SFANC algorithm acquires this joint frequency and direction information in advance, it can generate a more suitable control filter for the coming noise in a shorter time. By involving the Bhattacharyya distance Matrix (BdM) to simultaneously render spatial and frequency information of the primary noise, the proposed method further facilitates the precision of the filter selection for multichannel ANC systems. The BdM can also be adaptive to non-uniformed array. Moreover, experimental results demonstrate the stability of BdM spectra in detecting joint frequency and directional information and showcase the method's ability to accurately predict noise variation and respond promptly. Comparative analysis against other popular algorithms validates the superiority of our approach in dealing with dynamic and moving noise sources. Therefore, these theoretical investigations and numerical simulations show that this proposed method can potentially be applied to cope with the dynamic noise in real-world scenarios.