臺灣博碩士論文加值系統

本論文將探討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。我們考慮一語音及一噪音訊號經空間路徑傳遞後，由兩支麥克風量測到混合之訊號，然後將量測到之混合訊號透過具MJH演算法之前饋式神經網路（FFNN_MJH），將語音及噪音訊號分離出來，並提供予主動噪音控制器，使其能輸出適當之控制訊號去激發喇叭，產生控制聲波來消除其中一支麥克風周圍之噪音，並保留語音訊號。由電腦模擬結果中顯示本系統對於單頻及倍頻之噪音訊號，在消除噪音及保留語音訊號上皆具有良好之效果，且在估測控制路徑不準確時本系統依然擁有適當之強健性。

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.

論文目錄
頁次
誌謝 i
中文摘要 ii
英文摘要 iii
論文目錄 iv
表目錄 vi
圖目錄 vi
第一章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 3
1.3 論文概要 6
第二章 以獨立成分分析法為基礎之前饋式神經網路（FFNN_MJH） 7
2.1 遞迴式神經網路 7
2.2 統計獨立 9
2.3 JH演算法 10
2.4 前饋式神經網路 13
2.5 MJH演算法 15
2.6 盲訊號分離電腦模擬結果 19
第三章 主動噪音控制（ANC）系統：使用FFNN_MJH 23
3.1 FFNN_MJH-Based ANC系統架構 23
3.2 具FXLMS演算法之FIR適應性濾波器 25
3.3 ANC系統結合FFNN_MJH之應用 27
第四章 電腦模擬 33
4.1 FFNN_MJH-Based ANC系統電腦模擬之初始設定 33
4.2 FFNN_MJH-Based ANC系統於單頻噪音訊號之模擬結果 40
4.3 FFNN_MJH-Based ANC系統於倍頻噪音訊號之模擬結果 42
4.4 FFNN_MJH-Based ANC系統於倍頻噪音訊號放大五倍之
模擬結果 45
4.5 FFNN_MJH-Based ANC系統在估測控制路徑不準確時之模
擬結果 49
第五章 結論與未來展望 59
5.1 結論 59
5.2 未來展望 60
附錄A 獨立成分分析法（ICA） 61
A.1 ICA之基本概念 61
A.2 ICA之限制 64
附錄B 動態遞迴式神經網路 69
參考文獻 73

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