Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142040
Title: Feedforward selective fixed-filter active noise control : algorithm and implementation
Authors: Shi, Dongyuan
Gan, Woon-Seng
Lam, Bhan
Wen, Shulin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Shi, D., Gan, W.-S., Lam, B., & Wen, S. (2020). Feedforward selective fixed-filter active noise control : algorithm and implementation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 1479-1492. doi:10.1109/TASLP.2020.2989582
Project: COT-V4-2019-1
Journal: IEEE/ACM Transaction on Audio, Speech, and Language Processing
Abstract: Conventional real-time active noise control (ANC) usually employs the adaptive filtered-x least mean square (FxLMS) algorithm to approach optimum coefficients for the control filter. However, lengthy training is usually required, and the perceived noise reduction is not immediately realized. Motivated by the practical implementation, we propose a selective fixed-filter active noise control (SFANC) algorithm, which selects a pre-trained control filter to attenuate the detected primary noise rapidly. On top of improved robustness, the complexity analysis reveals that SFANC appears to be more efficient. The SFANC algorithm chooses the most suitable control filter based on the frequency-band-match approach implemented in a partitioned frequency-domain filter. Through simulations, SFANC is shown to exhibit a satisfactory response time and steady-state noise reduction performance, even for time varying noise and real nonstationary disturbance.
URI: https://hdl.handle.net/10356/142040
ISSN: 2329-9290
DOI: 10.1109/TASLP.2020.2989582
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TASLP.2020.2989582
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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