Deep Generative Fixed-filter Active Noise Control

• URL: http://arxiv.org/abs/2303.05788v1
• Date: Fri, 10 Mar 2023 08:47:22 GMT
• Title: Deep Generative Fixed-filter Active Noise Control
• Authors: Zhengding Luo, Dongyuan Shi, Xiaoyi Shen, Junwei Ji, Woon-Seng Gan
• Abstract summary: 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. The efficacy of the GFANC method is demonstrated by numerical simulations on real-recorded noises.
• Score: 17.42035489262148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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