Due to the slow convergence and poor tracking ability, conventional LMS-based
adaptive algorithms are less capable of handling dynamic noises. Selective
fixed-filter active noise control (SFANC) can significantly reduce response
time by selecting appropriate pre-trained control filters for different noises.
Nonetheless, the limited number of pre-trained control filters may affect noise
reduction performance, especially when the incoming noise differs much from the
initial noises during pre-training. Therefore, a generative fixed-filter active
noise control (GFANC) method is proposed in this paper to overcome the
limitation. Based on deep learning and a perfect-reconstruction filter bank,
the GFANC method only requires a few prior data (one pre-trained broadband
control filter) to automatically generate suitable control filters for various
noises. The efficacy of the GFANC method is demonstrated by numerical
simulations on real-recorded noises.Comment: Accepted by ICASSP 2023. Code will be available after publicatio
