Nonlinear Active Noise Control via Model-Based Approaches

Sam Chau Duong; Hiroshi Kinjo; Naoki Oshiro

doi:10.20965/jaciii.2011.p0854

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JACIII Vol.15 No.7 pp. 854-859

doi: 10.20965/jaciii.2011.p0854

(2011)

Paper:

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Nonlinear Active Noise Control via Model-Based Approaches

Sam Chau Duong, Hiroshi Kinjo, and Naoki Oshiro

Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa 903-0213, Japan

Received:

March 12, 2011

Accepted:

May 9, 2011

Published:

September 20, 2011

Keywords:

active noise control, model-based signal processing, neural network, engine noise control, traffic noise control

Abstract

Active noise control has attracted much research attention due to its several advantages over passive noise control. This paper introduces two model-based noise canceling techniques, that is, using the Moving Average (MA) model and a feedforward Neural Network (NN) to estimate the signal. The Least Mean Square (LMS) algorithm is used to minimize the error in the MA model while a backpropagation algorithm is employed to optimize the NN. Due to its advantages of good robustness and nonlinear processing, the NN is considered to be suitable for nonlinear signals. In order to reduce computational cost, the backpropagation algorithm in the NN is applied once at each time step with only one iteration. To examine the methods, two real-world problems are considered, one being engine noise and the other road traffic noise. A comparison between the two methods is carried out. Results indicate that both the MA and NN processors are effective in reducing the noises and that the NN based approach is superior over the MA model, especially for low frequency band.

Cite this article as:

S. Duong, H. Kinjo, and N. Oshiro, “Nonlinear Active Noise Control via Model-Based Approaches,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.7, pp. 854-859, 2011.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] L. Goines and L. Hagler, “Noise pollution: A modem plague,” Southern Medical J., Vol.100, No.3, pp. 287-294, March 2007.

[2] [2] W. Passchier-Vermeer and W. F. Passchier, “Noise exposure and public health,” Environmental Health Perspectives, Vol.108, Supplement 1: Reviews in Environmental Health, pp. 123-131, 2000.

[3] [3] M. J. Crocker (Ed.), “Handbook of Noise and Vibration Control,” John Wiley & Sons, Inc., NJ, 2007, USA.

[4] [4] B. Widrow et al., “Adaptive noise canceling: Principles and applications,” Proc. of the IEEE, Vol.63, No.12, pp. 1692-1716, 1975.

[5] [5] S. J. Elliott, I. M. Stothers, and P. A. Nelson, “A multiple error LMS algorithm and its application to the active control of sound and vibration,” IEEE Trans. Acoustics, Speech, and Signal Processing, Vol.35, pp. 1423-1434, 1987.

[6] [6] S. J. Elliot and P. A. Nelson, “Active noise control,” IEEE Signal Processing Magazine, Vol.10, pp. 12-35, 1993.

[7] [7] R. Shoureshi, “Active noise control: a marriage of acoustics and control,” American Control Conf., Vol.3, pp. 3444-3448, 1994.

[8] [8] M. E. Deisher and A. S. Spanias, “Adaptive noise cancellation using fast optimum block algorithms,” IEEE Int. Symposium on Circuits and Systems, Vol.1, pp. 698-701, 1991.

[9] [9] M. Tahernezadhi, R. V. Yellapantula, and S. C. Manapragada, “A subband AEC coupled with engine noise cancellation,” IEEE Int. Symposium on Circuits and Systems, ISCAS’96, Vol.2, pp. 241-244, 1996.

[10] [10] S. Nishimura, T. Usagawa, and S. Ise, “Active Noise Control,” Corona Publishing Co., Ltd, 2006 (in Japanese).

[11] [11] M. Bouchard, B. Paillard, and C. T. Le Dinh, “Improved training of neural networks for the nonlinear active control of sound and vibration,” IEEE Trans. Neural Networks, Vol.10, No.2, pp. 391-401, 1999.

[12] [12] D. Pavisic, L. Blondel, J.-P. Draye, G. Libert, and P. Chapelle, “Active noise control with dynamic recurrent neural networks,” Proc. European Symposium on Artificial Neural Networks, ESANN, pp. 45-50, 1995.

[13] [13] R. T. Bambang, “Adjoint EKF learning in recurrent neural networks for nonlinear active noise control,” Applied Soft Computing, Vol.8, pp. 1498-1504, 2008.

[14] [14] T. Soeda, T. Nakamizo, and S. Omatu, “Foundation and Application of the Signal Processing,” Nishin Syuppan, 1979 (in Japanese).

[15] [15] J. V. Candy, “Model-Based Signal Processing,” Wiley-IEEE Press, 2005.

[16] [16] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, Vol.323, pp. 533-536, 1986.