Deep Generative Fixed-Filter Active Noise Control

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...

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Bibliographic Details
Published inICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 5
Main Authors Luo, Zhengding, Shi, Dongyuan, Shen, Xiaoyi, Ji, Junwei, Gan, Woon-Seng
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.06.2023
Subjects
Active noise control
Broadband communication
convolutional neural network
deep learning
Feature extraction
Filter banks
Filtering algorithms
generative fixed-filter ANC
Noise reduction
Signal processing
Training
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10095205