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.
@article{arxiv.2303.05788,
title = {Deep Generative Fixed-filter Active Noise Control},
author = {Zhengding Luo and Dongyuan Shi and Xiaoyi Shen and Junwei Ji and Woon-Seng Gan},
journal= {arXiv preprint arXiv:2303.05788},
year = {2023}
}
Comments
Accepted by ICASSP 2023. Code will be available after publication