A new variable step size LMS algorithm-based method for improved online secondary path modeling in active noise control systems

• Published in: IEEE transactions on audio, speech, and language processing Vol. 14; no. 2; pp. 720 - 726
• Main Authors: Akhtar, M.T., Abe, M., Kawamata, M.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.03.2006; Institute of Electrical and Electronics Engineers
• Subjects: Active noise control; Active noise reduction; Adaptive filters; Algorithms; Applied sciences; Computational complexity; Control system synthesis; Convergence; Detection, estimation, filtering, equalization, prediction; Disturbances; Exact sciences and technology; FxLMS algorithm; Information, signal and communications theory; Least squares approximation; Mathematical models; modified FxLMS algorithm; Noise cancellation; Noise reduction; On-line systems; Online; online secondary path modeling; Signal and communications theory; Signal, noise; Size control; Stems; Telecommunications and information theory; variable step size least mean square (VSS LMS) algorithm; Variable structure systems; Performance evaluation; FxLMS algorithm; Target tracking; Computer simulation; Noise reduction; On-line systems; Adaptive filter; Algorithm; Computational complexity; Modeling; variable step size least mean square (VSS LMS) algorithm; modified FxLMS algorithm; online secondary path modeling; Indirect method; Active noise control; Least mean squares methods
• ISSN: 1558-7916
• DOI: 10.1109/TSA.2005.855829

This paper proposes a new method for online secondary path modeling in active noise control systems. The existing methods for active noise control systems with online secondary path modeling consist of three adaptive filters. The main feature of the proposed method is that it uses only two adaptive filters. In the proposed method, the modified-FxLMS (MFxLMS) algorithm is used in adapting the noise control filter and a new variable step size (VSS) least mean square (LMS) algorithm is proposed for adaptation of the secondary path modeling filter. This VSS LMS algorithm is different from the normalized-LMS (NLMS) algorithm, where the step size is varied in accordance with the power of the reference signal. Here, on the other hand, the step size is varied in accordance with the power of the disturbance signal in the desired response of the modeling filter. The basic idea of the proposed VSS algorithm stems from the fact that the disturbance signal in the desired response of the modeling filter is decreasing in nature, (ideally) converging to zero. Hence, a small step size is used initially and later its value is increased accordingly. The disturbance signal, however, is not available directly, and we propose an indirect method to track its variations. Computer simulations show that the proposed method gives better performance than the existing methods. This improved performance is achieved at the cost of a slightly increased computational complexity.