On the development of adaptive hybrid active noise control system for effective mitigation of nonlinear noise

The presence of nonlinearities as well as acoustic feedback deteriorates the cancellation performance of the conventional filtered-x LMS (FxLMS) algorithm based active noise control (ANC) systems. With an objective to improve the performance, a novel filtered-su LMS (FsuLMS) algorithm based ANC system which employs a convex combination of an adaptive IIR filter with a functional link artificial neural network (FLANN) is proposed in this paper. The corresponding learning algorithm of the ANC system is derived and used in the simulation study for performance evaluation. Simulation study reveals enhanced performance of the proposed system over that of its component filters.

[1]  Ganapati Panda,et al.  Active mitigation of nonlinear noise Processes using a novel filtered-s LMS algorithm , 2004, IEEE Transactions on Speech and Audio Processing.

[2]  Xiangping Zeng,et al.  Adaptive reduced feedback FLNN filter for active control of nonlinear noise processes , 2010, Signal Process..

[3]  Sang-Kwon Lee,et al.  Modified-filtered-u LMS algorithm for active noise control and its application to a short acoustic duct , 2011 .

[4]  Jiashu Zhang,et al.  Adaptively Combined FIR and Functional Link Artificial Neural Network Equalizer for Nonlinear Communication Channel , 2009, IEEE Transactions on Neural Networks.

[5]  Li Tan,et al.  Adaptive Volterra filters for active control of nonlinear noise processes , 2001, IEEE Trans. Signal Process..

[6]  L. Eriksson Development of the filtered‐U algorithm for active noise control , 1990 .

[7]  Sen M. Kuo,et al.  Active Noise Control Systems: Algorithms and DSP Implementations , 1996 .

[8]  G. L. Sicuranza,et al.  A Generalized FLANN Filter for Nonlinear Active Noise Control , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[9]  Ali H. Sayed,et al.  Mean-square performance of a convex combination of two adaptive filters , 2006, IEEE Transactions on Signal Processing.

[10]  Paul Strauch,et al.  Active control of nonlinear noise processes in a linear duct , 1998, IEEE Trans. Signal Process..

[11]  Giovanni L. Sicuranza,et al.  On the BIBO Stability Condition of Adaptive Recursive FLANN Filters With Application to Nonlinear Active Noise Control , 2012, IEEE Transactions on Audio, Speech, and Language Processing.