在陣列訊號處理、生物醫學分析、地質探測或語音處理等領域，目前的技術還無法直接控制傳送已知訊號。為了能在這些環境中分離出訊號源，盲訊號分離(Blind Source Separation)是很重要的研究課題，近幾年廣泛地被討論。在語音溝通方面，日常生活中我們時常會遇到一種不可避免的情況，必須要在吵鬧的聚會場合裡專心的聽到想要聆聽的聲音並且與人交談，而這時候我們耳朵所接受到的聲音卻是同時有很多人在講話，甚至還夾雜著電話鈴聲、背景音樂聲等，這也就是著名的雞尾酒派對問題(cocktail party problem)。本論文就是在探討盲訊號分離的的問題，主要目標在於尋找解分離混合矩陣並且把訊號來源從混合在一起的訊號中分離出來。基於共同對角化共變異矩陣和延遲時間的共變異矩陣為基礎，加上適當選擇延遲時間的輔助，我們利用原訊號與重建訊號之SIR值來評估分離的效能，並且拿來與其他著名的獨立成份分析法做比較，實驗結果顯示出此方法不僅擁有在分離效果上的優點，同時也擁有運算時間迅速的優點。同時，我們將此概念轉至頻域上再分別對每個頻率執行運算，由於在頻域上會有排列問題和膨脹問題的現象發生因此在實驗中加入了解決此問題的演算法加以討論，最後將重組完後的頻譜利用反傅立葉轉換轉回時域以成功的達成分離訊號的目地。

[1] L. Tong, V. C. Soon, Y. -F. Huang and R. Lin, “AMUSE: A new blind identification algorithm,” Proc.IEEE International Symposium on Circuits and Systems, New Orleans, LA., May 1-3, 1990, vol. 3, pp. 1784-1787.
[2] J. Herault and C. Jutten, “Space or time adaptive signal processing by neural network models,” in Proc. AIP Conf., J. S. Denker, Ed., Snowbird,
UT, 1986, pp. 206–211.
[3] P. Comon, “Independent component analysis—A new concept?,” Signal Processing, vol. 36, pp. 287–314, 1994.
[4] A. J. Bell and T. J. Sejnowski, “An information-maximization approach
to blind separation and blind deconvolution,” Neural Comput., vol. 7,pp. 1129–1159, 1995.
[5] A. Hyvärinen and E. Oja, “A fast fixed-point algorithm for independent
component analysis,” Neural Comput., vol. 9, pp. 1483–1492, 1997.
[6] P. Comon, “Tensor diagonalization, a useful tool in signal processing,” in IFAC-SYSID, 10th IFAC Symposium on System Identification, M. Blanke
and T. Soderstrom, Eds., vol. 1, Copenhagen, Denmark, 1994, pp. 77–82.
[7] J.-F. Cardoso and A. Souloumiac, “Blind beamforming for non Gaussian
signals,” IEE Proceedings-F, vol. 140, pp. 362–370, 1993.
[8] T.-W. Lee, M. Girolami, and T. Sejnowski, “Independent component
analysis using an extended infomax algorithm for mixed sub-gaussian
and super-gaussian sources,” Neural Computation, vol. 11, no. 2, pp.
409–433, 1999.
[9] S. Amari, A. Cichocki, and H. H. Yang, “A new learning algorithm for
blind signal separtion,” in Advances in Neural Information Processing
Systems, vol. 8, D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, Eds.
Cambridge, MA, 1996, pp. 757–763.
[10] R. Boscolo, HP Pan, and VP Roychowdhury. Independent component analysis based on nonparametric density estimation. IEEE Transactions on Neural Networks, 15:55-65, 2004.
[11] S. Kurita, H. Saruwatari, S. Kajita, K. Takeda, and F. Itakura, "Evaluation of blind signal separation method using directivity pattern under reverberant conditions," Proc. ICASSP2000, pp.3140--3143, 2000.
[12] N. Murata, S. Ikeda, and A. Ziehe.”An approach to blind source separation based on temporal structure of speech signals.” In Proceedings of 1998 International Conference on Artificial Neural Networks, Skovde, September 1998.ICANN’98.
[13] DT Pham, Ch. Servi`ere, and H. Boumaraf, “Blind separation. of convolutive audio mixtures using nonstationarity”, Proceeding of ICA 2003 Conference, pp. 975-980 , Nara, Japan April 2003
[14] L. Molgedey and H. G. Schuster. “Separation of a mixture of independent signals using time delayed correlations.” Phys. Rev. Lett., 72(23):3634–3637,1994.
[15] Andreas Ziehe, Klaus-Robert Müller,“TDSEP an efficient algorithm for blind separation using time structure,” ICANN, 1998.