Please use this identifier to cite or link to this item:
Title: Using empirical wavelet transform to speed up selective filtered active noise control system
Authors: Wen, Shulin
Gan, Woon-Seng
Shi, Dongyuan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Wen, S., Gan, W.-S., & Shi, D. (2020). Using empirical wavelet transform to speed up selective filtered active noise control system. The Journal of the Acoustical Society of America, 147(5), 3490–3501. doi:10.1121/10.0001220
Project: COT-V4-2019-1
Journal: The Journal of the Acoustical Society of America
Abstract: The gradual adaptation and possibility of divergence hinder the active noise control system from being applied to a wider range of applications. Selective active noise control has been proposed to rapidly reduce noise by selecting a pre-trained control filter for different primary noise detected without an error microphone. For stationary noise, considerable noise reduction performance with a short selection period is obtained. For non-stationary noise, more restrictive requirements are imposed on instant convergence, as it leads to faster tracking and better noise reduction performance. To speed up a selective filtered active noise control system, empirical wavelet transform is introduced here to accurately and instantaneously extract the frequency information of primary noise. The boundary of the first intrinsic mode function of random noises is extracted as the instant signal feature. Primary noise is attenuated immediately by picking the optimal pre-trained control filter labeled by the nearest boundary. The storage requirement for a pre-trained control filter library is reduced. Instant control is obtained, and the instability caused by output saturation is overcome. With more concentrated energy distribution, better noise reduction performance is achieved by the proposed algorithm compared to conventional and selective active noise control algorithms. Simulation results validate these advantages of the proposed algorithm.
ISSN: 0001-4966
DOI: 10.1121/10.0001220
Rights: © 2020 Acoustical Society of America. All rights reserved. This paper was published in The Journal of the Acoustical Society of America and is made available with permission of Acoustical Society of America.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.