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

• Published in: IEEE/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
• Language: English
• 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
• ISSN: 2329-9290; 2329-9304; 2329-9304
• DOI: 10.1109/TASLP.2020.2989582

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.