Robust Q-Gradient Subband Adaptive Filter for Nonlinear Active Noise Control

• Published in: IEEE/ACM transactions on audio, speech, and language processing Vol. 29; pp. 2741 - 2752
• Main Authors: Yin, Kai-Li, Pu, Yi-Fei, Lu, Lu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active control; Active noise control; Adaptive algorithms; Adaptive control; Adaptive filters; Algorithms; Artificial neural networks; Convergence; Functional link artificial neural network (FLANN); Low noise; maximum correntropy criterion (MCC); Noise control; nonlinear active noise control (NLANC); Nonlinear control; Robustness; Speech processing; subband adaptive filter (SAF)
• ISSN: 2329-9290; 2329-9304
• DOI: 10.1109/TASLP.2021.3102193

Active noise control (ANC) is gaining attention for attenuating noise from a remote location. Considering the problem of nonlinear active noise control (NLANC) at a virtual location, a robust filtered-s subband adaptive filtering algorithm based on the <inline-formula><tex-math notation="LaTeX">q</tex-math></inline-formula>-gradient maximum correntropy criterion (RFsSAF-qMCC) is proposed in this paper. The proposed RFsSAF-qMCC algorithm develops the functional link artificial neural network (FLANN)-SAF structure as the controller, and embeds the MCC with the concept of q -gradient, thereby improving the convergence speed in the impulsive environment. To solve the trade-off between fast convergence and low noise residue caused by the fixed q -gradient, a variable q -gradient algorithm, termed as RFsSAF-vqMCC, is further developed. As an additional contribution, the convergence behavior of the proposed RFsSAF-qMCC and RFsSAF-vqMCC algorithms is analyzed. Simulation results corroborate the effectiveness of the proposed algorithms as compared to state-of-the-art algorithms.