A Variable Step-Size FXLMS Algorithm for Narrowband Active Noise Control

• Published in: IEEE transactions on audio, speech, and language processing Vol. 21; no. 2; pp. 301 - 312
• Main Authors: Huang, Boyan, Xiao, Yegui, Sun, Jinwei, Wei, Guo
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.02.2013; Institute of Electrical and Electronics Engineers
• Subjects: Algorithm design and analysis; Applied sciences; Convergence; Convergence rate; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Finite impulse response filter; FXLMS; FXRLS; Information, signal and communications theory; Least squares approximation; Narrowband; narrowband active noise control (NANC); Noise; Signal and communications theory; Signal processing algorithms; Signal, noise; Telecommunications and information theory; variable step size; Performance evaluation; Infinite impulse response filter; FXLMS; FXRLS; Narrow band noise; Background noise; Algorithm; Computational complexity; Variable step method; Convergence speed; Simulation; Least squares method; Convergence rate; Stationary noise; Active noise control; narrowband active noise control (NANC); Least mean squares methods; variable step size
• ISSN: 1558-7916; 1558-7924
• DOI: 10.1109/TASL.2012.2223673

In this paper, a variable step-size filtered-x LMS (VSS-FXLMS) algorithm is proposed for a typical narrowband active noise control system. The new algorithm converges much faster than the conventional FXLMS algorithm does, and indicates a convergence rate quite similar to that of the filtered-x recursive least square (FXRLS) algorithm in stationary noise environments. It also considerably outperforms these two existing algorithms in nonstationary situations. The proposed algorithm requires some more computations as compared with the FXLMS algorithm; however, its computational complexity is significantly less than that of the FXRLS algorithm. Numerous simulations for stationary and nonstationary scenarios are conducted to demonstrate the superior performance of the proposed VSS-FXLMS algorithm as compared with the FXLMS and the FXRLS algorithms.