Feedforward Selective Fixed-Filter Active Noise Control: Algorithm and Implementation

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. Motivat...

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Published inIEEE/ACM transactions on audio, speech, and language processing Vol. 28; pp. 1479 - 1492
Main Authors Shi, Dongyuan, Gan, Woon-Seng, Lam, Bhan, Wen, Shulin
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Active control
Active noise control
Adaptive algorithms
Adaptive control
Adaptive filters
Algorithms
Computer simulation
Feature extraction
Feedforward control
filtered-x least mean square
Frequency control
frequency-domain filtering
Noise control
Noise reduction
Optimized production technology
Response time
selective fixed-filter active noise control
Signal processing algorithms
Speech processing
Training
Online AccessGet full text
ISSN2329-9290
2329-9304
2329-9304
DOI10.1109/TASLP.2020.2989582

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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 pretrained 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 non-stationary disturbance.
AbstractList 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 pretrained 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 non-stationary disturbance.
Author Shi, Dongyuan
Gan, Woon-Seng
Wen, Shulin
Lam, Bhan
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SubjectTerms Active control
Active noise control
Adaptive algorithms
Adaptive control
Adaptive filters
Algorithms
Computer simulation
Feature extraction
Feedforward control
filtered-x least mean square
Frequency control
frequency-domain filtering
Noise control
Noise reduction
Optimized production technology
Response time
selective fixed-filter active noise control
Signal processing algorithms
Speech processing
Training
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Title Feedforward Selective Fixed-Filter Active Noise Control: Algorithm and Implementation
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