獨立成份分析法於真實環境中聲音訊號分離之探討

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：20

、訪客IP：18.117.183.150

姓名

蔡政哲(Cheng-Tse Tsai) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

獨立成份分析法於真實環境中聲音訊號分離之探討
(A Study of Field Audio Signal Separation by Using Independent Component Analysis)

相關論文

★ 口腔核磁共振影像的分割與三維灰階值內插	★ 數位式氣喘尖峰氣流量監測系統設計
★ 結合人工電子耳與助聽器對中文語音辨識率的影響	★ 人工電子耳進階結合編碼策略的中文語音辨識成效模擬--結合助聽器之分析
★ 中文發聲之神經關聯性的腦功能磁振造影研究	★ 利用有限元素法建構3維的舌頭力學模型
★ 以磁振造影為基礎的立體舌頭圖譜之建構	★ 腎小管之草酸鈣濃度變化與草酸鈣結石關係之模擬研究
★ 口腔磁振影像舌頭構造之自動分割	★ 微波輸出窗電性匹配之研究
★ 以軟體為基準的助聽器模擬平台之發展-噪音消除	★ 以軟體為基準的助聽器模擬平台之發展-回饋音消除
★ 模擬人工電子耳頻道數、刺激速率與雙耳聽對噪音環境下中文語音辨識率之影響	★ 用類神經網路研究中文語音聲調產生之神經關聯性
★ 教學用電腦模擬生理系統之建構	★ 以軟體為基準的助聽器模擬平台之發展-方向性麥克風

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

中文摘要
本論文的主要工作在透過真實環境中的混合聲音，特別是語音，驗証獨立成份分析法(Independent Component Analysis, ICA)能夠擷取在兩獨立聲源中具有時間延遲之聲源的特性，目的是希望藉由ICA對聲音訊號分離(audio signal separation)的結果來了解ICA在實際應用上的限制與特性。就理論而言，在統計獨立的假設前提下，ICA可以分離出即時混合(instantaneous mixing)的聲音訊號。然而，環境中的聲音訊號有時間延遲(time delay)、迴響(reverberation)的情況，使得觀察到的訊號為一迴旋混合(convolutive mixing)，造成ICA無法正確地分離出獨立聲源。由我們的理論推導發現，在只考慮兩個聲源及兩支麥克風的情況下，若其中一個聲源在兩支麥克風之間有時間延遲，而另一個聲源沒有，則具有時間延遲的聲源可以被ICA分離出來。最後透過在真實環境中的聲音錄製，實驗証實其結果與理論似乎相吻合。

摘要(英)

Abstract
The main purpose of this study is to understand the characteristics of independent component analysis (ICA) method and its limits in application of audio signal separation. Theoretically, ICA may separate the instantaneous mixing audio signal under the assumption of mutual statistical independence. However, under the effects of time delay and reverberation, the field-recorded audio signals could be convolutively mixed and could not be correctly separated by the ICA. From our theoretical derivation, the audio source with time delay could be separated by the ICA from the mixed signals that were recorded with two microphones under the condition of one audio source with time delay and the other without. In this study, our theoretical derivation was further tested with field audio signal recordings and the results seem to fit theoretical derivation well.

關鍵字(中)

★ 聲音訊號分離
★ 獨立成份分析法
★ 迴旋混合
★ 迴響

關鍵字(英)

★ Reverberation
★ Convolutive Mixing
★ Independent Component Analysis
★ Audio Signal Separation

論文目次

目錄
中文摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 ?
第一章緒論 1
1.1 研究動機 1
1.2 ICA簡介 2
1.3 ICA文獻探討 4
1.4 論文內容架構 7
第二章獨立成份分析法原理 8
2.1 ICA線性模型 8
2.2 ICA基本架構 12
2.2.1 資料前處理 12
2.2.2 目標函數與最佳化演算法 14
2.2.3 獨立性量測 16
2.2.3.1 非高斯特性 16 IV
2.2.3.2 交互資訊 18
2.2.3.3 非線性不相關 19
第三章獨立成份分析演算法 20
3.1 基於資訊理論的ICA 20
3.1.1 FastICA演算法 20
3.1.2 InfomaxICA演算法 24
3.2 時域ICA與頻域ICA 29
第四章實驗設備與方法 32
4.1 實驗設備 32
4.2 實驗方法 32
4.2.1 理論依據 32
4.2.2 實驗設計與流程 37
第五章結果與討論 49
5.1 實驗一之ICA分離結果 49
5.2 實驗二之ICA分離結果 55
5.3 實驗三之ICA分離結果 57
5.4 實驗四之ICA分離結果 59
5.5 比較InfomaxICA與FastICA的分離結果 62
第六章結論與未來展望 64 V
6.1 結論 64
6.2 未來展望 65
參考文獻 66
附錄A 71

參考文獻

參考文獻
Arora, J. S., “Introduction to Optimum Design”, McGraw Hill, New York, 1989.
Bell, A. J., Sejnowski, T. J., “An information maximisation approach to blind separation and blind deconvolution”, Neural Computation, Vol.7, pp.1129-1159, 1995.
Bell, A. J., Sejnowski, T. J., “Learning the higher-order structure of a natural sound”, Network: Computation in Neural Systems, 7:261-266, 1996.
Bouzaien, M., Mansour, A., “HOS Criteria & ICA Algorithms Applied to Radar Detection”, 4th International Symposium on Independent
Component Analysis and Blind Signal Separation (ICA2003), Nara,
Japan, April, 2003.
Cover, T. M., and Thomas, J. A., “Elements of Information Theory”, A Wiley-Interscience Publication, 1991.
Comon, P., “Independent Component Analysis, A New Concept?”, Signal Processing , Vol.36, no.3, pp.287-314, 1994.
Comon, P., “Contrasts for multichannel blind deconvolution”, Signal Processing Letters, 3(7):209-211, 1996.
Chih-I Hung, Po-Lei Lee, Yu-Te Wu, Li-Fen Chen, Tzu-Chen Yeh, Jen-Chuen Hsieh, “Recognition of Motor Imagery
Electroencephalography Using Independent Component Analysis and Machine Classifiers”, Annals of Biomedical Engineering 33, 1053-1070, 2004.
Haykin, S., “Blind Deconvolution”, Prentice-Hall, New Jersey, 1994.
66
Himberg, J., and Hyv?rinen, A., “Independent component analysis for binary data:An experimental study”, In Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001), San Diego, California, 2001.
Hurri, J., Hyv?rinen, A., Karhunen, J., and Oja, E., “Image feature extraction using independent component analysis”, In Proc. NORSIG’96, pages 475–478, Espoo,Finland, 1996.
H?rault, J., Jutten, C., and Ans, B., “D?tection de grandeurs primitives dans un message composite par une architecture de calcul neuromim?tique en apprentissage non supervis?”, in Actes du X?me colloque GRETSI, Nice, France, 20-24, pp.1017–1022, May, 1985.
Hyv?rinen, A., Oja, E., “A Fast Fixed-Point Algorithm for Independent Component Analysis”, Neural Computation, vol.9, pp.1483-1492, 1997.
Hyv?rinen, A., “Independent component analysis for time-dependent stochastic processes”, In Proc. Int.Conf. on Artificial Neural Networks (ICANN’98), pages 135–140, Skovde, Sweden, 1998.
Hyv?rinen, A., “Survey on independent component analysis”, Neural Computing Surveys 2, 94–128. 1999.
Hyv?rinen, A., “Gaussian moments for noisy independent component analysis”, IEEE Signal Processing Letters, 6(6):145–147, 1999.
Hyv?rinen, A., “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis”, IEEE Transactions on Neural Networks, Vol.10, No.3, pp.626–634, 1999.
Hyv?rinen, A., Pajunen, P., “Nonlinear independent component analysis: Existence and uniqueness results”, Neural Networks, 12(3):429–439, 1999.
67
Hyv?rinen, A. and Oja, E., “Independent Component Analysis: Algorithms and Applications”, Neural Networks, Vol.13(4-5),
pp.411–430, 2000.
Hyv?rinen, A., and Hoyer, P., “Emergence of phase and shift invariant features by decomposition of natural images into independent feature
subspaces”, Neural Computation, 12, 1705–1720, 2000.
Hyv?rinen, A., “Blind source separation by nonstationarity of variance: A cumulantbased approach”, IEEE Transactions on Neural Networks,
12(6):1471–1474, 2001.
Hyv?rinen, A., Karhunen, J., Oja, E., “Independent Component Analysis”, Wiley, New York, 2001.
Jutten, C., and Herault, J., “Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture”, Signal Processing, 24:1–10, 1991.
Lee, T.-W., “Independent Component Analysis: Theory and Applications”, Kluwer Academic Publishers, Boston, 1998.
Lee, T.-W., “Nonlinear Approaches to Independent Component Analysis”, Proceedings of the American Institute of Physics, Oct. 1999.
Lee, T.-W., “Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources”, Neural Computation, 11(2), 409-433, 1999.
Lee, J.-H., Jung, H.-J., Lee, T.-W., and Lee, S.-Y., “Speech Feature Extraction Using Independent Component Analysis”, IEEE International Conference on Acoustics, Speech and Signal Processing, III 1631-4, June 2000.
Lee, P.-L., Wu, Y.-T., Chen, L.-F., Chen, Y.-S., Cheng, C.-M., Yeh, T.-C.,
68
Ho, L.-T., Chang, M.-S., Hsieh, J.-C., “ICA-based spatiotemporal
approach for single-trial analysis of post-movement MEG beta synchronization”, Neuroimage, 20, pp. 2010-2030, 2003.
Linsker, R., “Local synaptic learning rules suffice to maximize mutual information in a linear network”, Neural Comp., 4, 691–702, 1992.
Lindsay, I, Smith., “A tutorial on Principal Components Analysis”, February 26, 2002.
Makeig, S., Bell, A, J., Jung, T., Sejnowski, T, J., “Independent Component Analysis of Electroencephalographic data”, Advances in Neural Information Processing System”, vol.8, pp. 145-151, 1996.
Murata, N., Ikeda, S., and Ziehe, A., “An approach to blind source separation based on temporal structure of speech signals”, Technical report, RIKEN BSI, 1998.
Nadal, J.-P., and Parga, N., “Non-linear neurons in the low-noise limit: a factorial code maximises information transfer”, Network, 4:295-312, 1994.
Pham, D.-T., and Garrat, P., “Blind separation of mixture of independent sources through a quasimaximum likelihood approach”, IEEE Trans. on Signal Processing, 45(7):1712–1725, 1997.
P. L. Lee., Y. T. Wu., L. F. Chen., S. S. Chen., T. C. Yeh., L.T. Ho., MD, and J. C. Hsieh., “Single-trial analysis of post-movement MEG beta synchronization using independent component analysis (ICA)”, Proceeding of International Joint Conference on Neural Network, 2003.
Yu-Te Wu, Po-Lei Lee, Li-Fen Chen, Tzu-Chen Yeh, Jen-Chuen Hsieh, Low-Tone Ho, “ICA-based analysis of movement-related modulation on alpha and beta activity of single-trial MEG measurement using spatial
69
and temporal templates”, Proceedings of the 1st International IEEE EMBS, Conference on Neural Engineering, pp.296-398, 2003.
Yu-Te Wu, Po-Lei Lee, Li-Fen Chen, Tzu-Chen Yeh, Jen-Chuen Hsieh, “Single-trial Quantification of Imagery Beta-band Mu Rhythm in Finger Lifting Task Using Independent Component Analysis (ICA)”, Proceedings of the 13th international conference on Biomagnetism, pp. 1045-1047, 2002.
張嘉芳, “以FastICA為基礎之時域聲音分離演算法”, 國立交通大學電機與控制工程研究所碩士論文, July, 2003.
陳彥名, “以獨立成分分析法萃取背景噪音中語音訊號之研究--於助聽器可能之應用”, 國立陽明大學醫學工程研究所碩士論文, June, 2004.
連憶如, “頻域獨立成分分析法於語音訊號分離之研究”, 國立交通大學電機與控制工程研究所碩士論文, July, 2004.
蔡政哲、吳炤民,“獨立成份分析法於真實環境中聲音訊號分離之探討”, 工程科技與中西醫學應用研討會會議論文, 2006.

指導教授

吳炤民(Chao-Min Wu)

審核日期

2006-7-22

推文