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標題: 以獨立成分分析法為基礎之前饋式神經網路於主動噪音控制之應用
Independent Component Analysis-Based Feed-Forward Neural Network for Applications of Active Noise Control
作者: 黃晧倫
Huang, Hau-Luen
關鍵字: 主動噪音控制;ANC;獨立成分分析法;ICA
出版社: 機械工程學系所
引用: 參考文獻 [1] Lueg, P., “Process of Silencing Sound Oscillations”, U.S. Patent, No.2043416, 1936. [2] Widrow, B. and Hoff, M. E., “Adaptive Switching Circuits”, IRE WESCON Conv. Rec, part 4, pp. 96-104, 1960. [3] Burgess, J. C., “Active Adaptive Sound Control in a Duct: A Computer Simulation”, J. Acoust. Soc. Am., Vol. 70, pp. 715-726, 1981. [4] Kuo, S. M. and Morgan, D. R., Active Noise Control System: Algorithms and DSP Implementations, 605 Third Avenue, New York, 10158-0012, 1996. [5] Kuo, S. M. and Morgan, D. R., “Active Noise Control: A Tutorial Review”, Proceeding of the IEEE, Vol. 87, pp. 943-975, 1999. [6] Jutten, C. and H´erault, J., “Independent component analysis (INCA) versus principal component analysis”, J.L. Lacoume et al.,editor, Signal Processing IV: Theories and Applications, Elsevier, pages 643–646, 1988. [7] Bell, A.J. and Sejnowski, T.J., “A non-linear information maximization algorithm that performs blind separation”, Advances in Neural Information Processing System 7, pages 467-474. The MIT Press, Cambridge, MA, 1995. [8] Bell, A.J. and Sejnowski , T.J., “An information-maximization approach to blind separation and blind deconvolution”, Neural Computation, 7:1129-1159, 1995. [9] Hyvärinen, Aapo, “A family of fixed-point algorithms for independent component analysis”, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP’97), pages 3917-3920, Munich, Germany, 1997. [10] Hyvärinen, Aapo Oja, E., “A fast fixed-point algorithm for independent component analysis”, Neural Computation, 9(7):1483-1492, 1997. [11] F. Sattar, M.Y. Siyal, L.C.Wee and L.C. Yen, “Blind source separation of audio signals using improved ICA method”, Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on, pages:452 – 455, 2001. [12]林巧苑,獨立成分分析法應用於磁振腦血流灌注研究之評估,國家圖書館,2001 [13] Nakamura, H. Masaki, Y. Kotani, M. Akazawa, K. Moritani T., “The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains:part I”, Journal of Electromyography and Kinesiology 14 423-432, 2004. [14] Jun-il, Sohn and Minho, Lee, “Selective attention system using new active noise controller”, Kyungpook National University, Neurocomputing, Volume 31, Number 1, pp. 197-204(8), 2000. [15] Cichocki, and Amari, Adaptive Blind Signal and Image Processing, John Wiley & Sons, Ltd, 2000. [16] Choi, S. Cichocki, A., “Adaptive blind separation of speech signals: Cocktail party problem”, International Conference on Speech Processing, pp. 617-622, 1997. [17] Hyvärinen, Aapo, Independent Component Analysis, John Wiley, 2001. [18] 連億如,頻域獨立成分分析法於語音訊號分離之研究,交大碩士論文,2004

In this study, an application of active noise control (ANC) using an independent component analysis-based feed-forward neural network (FFNN) is investigated. We consider a speech source and a noise source generating mixture signals in the space. Two microphones located at two distinct places are used to measure the mixture signals. Since the signal sources and the transmission paths are unknown, we apply a FFNN with MJH algorithm(FFNN_MJH)for the measured signals to obtain estimates of the signal sources. These estimates are then used for an ANC controller such that suitable control signal can be generated to drive a loudspeaker. It is desired that one of the microphone can observe the speech while ignoring the noise by use of the loudspeaker. Computer simulation shows that the observed microphone can effectively retain the speech while attenuating the noise with respective to tonal or harmonic noises. The proposed system also maintains a certain degree of robustness with respective to the uncertainty in the transmission paths of the loudspeaker, demonstrating its feasibility.
其他識別: U0005-2708200711441200
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