基于独立分量分析的盲源分离方法的研究
摘要
盲源分离问题是目前信号处理领域中最热门的新兴技术之一。本文讨论了盲源分离的数学模型、实现方法等，并回顾和总结了多种典型的基于独立分量分析的盲源分离方法。蚁群算法作为新兴的智能优化算法，具有全局寻优能力、正反馈特性、强鲁棒性等特点，利用蚁群算法对代价函数进行全局寻优，可以很好的克服梯度算法容易陷入局部最小的缺陷，因此将它应用于盲源分离问题的求解。
针对超定盲源分离问题，本文从分离矩阵的奇异值分解出发，采用基于最小互信息的超定盲源分离代价函数，并用蚁群算法对代价函数进行优化，最终解决了一类超定的盲源分离问题；对于同步盲信号提取问题的研究，采用Schmidt消源去相关的方法避免了蚁群算法对已提取信号的重复提取，提高了算法的计算效率。
本文对非线性盲源分离问题也进行了阐述，针对后非线性混合模型，从基于联合累积量的非线性统计独立性判据出发，提出了基于该准则与蚁群算法相结合的非线性盲源分离算法。仿真表明该算法对后非线性混合盲源分离问题的可行性和有效性。
最后，开发了基于DSP的盲源分离实验平台，该平台是基于DSP+RTDX+PC／Windows构架搭建的，借助DSP实现盲源分离核心算法的实时运算，利用RTDX技术实现PC主机和DSP的双向实时数据传输，开发主机端界面来进行界面操作并显示实验结果。该实验平台的开发为借助DSP进行盲源分离实验提供了便利。
Blind Source Separation (BSS) has attracted a great deal of attentions from the signal processing community recently. The thesis not only deals with the BSS models and realization method, but also reviews some existed typical BSS algorithms based on Independent component analysis (ICA). Ant Colony Optimization (ACO) algorithm is a new intelligent optimization algorithm with the ability of global optimization, positive feedback characteristics and robust. A new BSS algorithm based ACO is proposed, which can avoid the problem that many algorithms based on gradient descent approach get into part extremum.
The problem of over-determined BSS is considered. Beginning with the Singular Value Decomposition (SVD) of the separation matrix, a cost function is presented based on Minimization of Mutual Information (MMI), and then the ACO algorithm is developed. Computer simulations verify the validity of the algorithm. The Blind Source Extraction (BSE) based on ACO is considered. A deflation algorithm based on Schmidt orthogonal is proposed, which can avoid extract signals repeatedly.
Research on nonlinear Blind Source Separation (NBSS). Network structure of post nonlinear BSS is introduced. A NBSS criteria based on cross-cumulates of output signals in higher order statistics is put forward and a novel NBSS algorithm based on the criteria and ACO is proposed. The simulation result demonstrates the effectiveness of the proposed NBSS algorithm.
A BSS experiment plantform based on DSP is developed. The platform is based on the DSP+RTDX+
PC/Windows frame structures, Using DSP to achieve BSS algorithm, Using RTDX for host and target plate bi-directional real-time transmission and developing a good host GUI for operation. The platform is easy to use and provides a good environment for BSS experiment.
针对超定盲源分离问题，本文从分离矩阵的奇异值分解出发，采用基于最小互信息的超定盲源分离代价函数，并用蚁群算法对代价函数进行优化，最终解决了一类超定的盲源分离问题；对于同步盲信号提取问题的研究，采用Schmidt消源去相关的方法避免了蚁群算法对已提取信号的重复提取，提高了算法的计算效率。
本文对非线性盲源分离问题也进行了阐述，针对后非线性混合模型，从基于联合累积量的非线性统计独立性判据出发，提出了基于该准则与蚁群算法相结合的非线性盲源分离算法。仿真表明该算法对后非线性混合盲源分离问题的可行性和有效性。
最后，开发了基于DSP的盲源分离实验平台，该平台是基于DSP+RTDX+PC／Windows构架搭建的，借助DSP实现盲源分离核心算法的实时运算，利用RTDX技术实现PC主机和DSP的双向实时数据传输，开发主机端界面来进行界面操作并显示实验结果。该实验平台的开发为借助DSP进行盲源分离实验提供了便利。
Blind Source Separation (BSS) has attracted a great deal of attentions from the signal processing community recently. The thesis not only deals with the BSS models and realization method, but also reviews some existed typical BSS algorithms based on Independent component analysis (ICA). Ant Colony Optimization (ACO) algorithm is a new intelligent optimization algorithm with the ability of global optimization, positive feedback characteristics and robust. A new BSS algorithm based ACO is proposed, which can avoid the problem that many algorithms based on gradient descent approach get into part extremum.
The problem of over-determined BSS is considered. Beginning with the Singular Value Decomposition (SVD) of the separation matrix, a cost function is presented based on Minimization of Mutual Information (MMI), and then the ACO algorithm is developed. Computer simulations verify the validity of the algorithm. The Blind Source Extraction (BSE) based on ACO is considered. A deflation algorithm based on Schmidt orthogonal is proposed, which can avoid extract signals repeatedly.
Research on nonlinear Blind Source Separation (NBSS). Network structure of post nonlinear BSS is introduced. A NBSS criteria based on cross-cumulates of output signals in higher order statistics is put forward and a novel NBSS algorithm based on the criteria and ACO is proposed. The simulation result demonstrates the effectiveness of the proposed NBSS algorithm.
A BSS experiment plantform based on DSP is developed. The platform is based on the DSP+RTDX+
PC/Windows frame structures, Using DSP to achieve BSS algorithm, Using RTDX for host and target plate bi-directional real-time transmission and developing a good host GUI for operation. The platform is easy to use and provides a good environment for BSS experiment.
引文
【1】 Amari S and Cichocki A. Adaptive blind signal processing: Neural network approaches. Proc of the IEEE[J], 1998, Vol. 86(10): 2026-2048
【2】 Cardoso J E Blind Signal separation: statistical principles. Proc of the IEEE[J], 1998, Vol.86(10): 2009-2025
【3】 Amari S I, Cichocki A and Yang H H. Blind Signal Separation and Extraction: Neural and Information-theoretic approaches. In: S. Haykin, editor, Unsupervised Adaptive Filtering [C]. New York: John Wiley & Sons, 2000, Vol.1: 103-104
【4】 Hyvarinen A, Karhunen J, Oja E. Independent Component Analysis [M]. New York: John Wiley & Sons, 2001
【5】 Cichocki A，Amari S等著．自适应盲信号与图像处理[M]．吴正国等译．北京：电子工业出版社，2005．5
【6】 余英林，谢胜利，蔡汉添等．信号处理新方法导论[M]．北京：清华大学出版社，2004
【7】 Tan Y and Wang J. Nonlinear Blind Source Separation Using Higher Order Statistics and a Genetic Algorithm. IEEE Trans On Evolutionary Computation[J], 2001, Vol.5(6): 600-612
【8】 章晋龙，何昭水，谢胜利．基于遗传算法的有序盲信号提取．电子学报[J]．2004，Vol．32(4)：616-619
【9】 Cao X R, Liu R W. A general approach to blind source separation [J]. IEEE Trans. Signal Processing, 1996, Vol. 44(3): 562-571.
【10】 Cichocki A, Thawonmas R, Amari S. Sequential blind signal extraction in order specified by stochastic properties[J]. Electronics Letters, 1997, Vol. 33(1, 2): 64-65
【11】 M. Dorigo, L M. Gambardella. Ant colony system: A cooperative learning approach to the traveling salesman problem[J], IEEE Trans on Evalutionary Computering, 1997, Vol .1(1):53-56.
【12】 李士勇等编著 蚁群算法及其应用 哈尔滨：哈尔滨工业大学出版社，2004．9
【13】 Ding LIU, Fei WANG. BSSLAB:A Toolbox for Blind Signal Separation and Independent Component Analysis. 2005 International Workshop on SCILAB and Open Source Software Engineering, Wuhan, P.R.China, 2005:77-83
【14】 TI.TMS320C6000 CPU and Instruction Set Reference Guide.2003.
【15】 苏涛等编著．DSP实用技术．西安：西安电子科技大学出版社．2002．
【16】 TMS320C64x RTDX Help (SPRH053). Texas Instruments, 1998
【17】 顾长怡，黄佩伟．利用RTDX技术实现计算机实时话音分析计算机工程[J] 2001，Vol．27(2)：155-156
【18】 TI. TMS320DM642 Evaluation Module quick start installation guide. 2003
【19】 Jutten C, Herault J., Blind separation of sources, Part Ⅰ: An adaptive algorithmbased on neuromimetic. Signal Processing[J], 1991, 24(1):1-10
【20】 Cardoso J F and Souloumiac A., Blind beam forming for non Gaussian signals. In IEE Proceedings-F[J], 1993, 140(6): 362-370
【21】 Comon P. Independent component analysis-a new concept. Signal Processing[J], 1994,36(3): 287-314
【22】 Puntonet C G, Pdeto A, Ortega J. New geometrical approach for blind separation of sources mapped to a neural network. International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing[C], 1996:174-182
【23】 Hyvarinen A and Oja E. A Fast Fixed-Point Algorithm for Independent Component Analysis. Neural Computation[J], 1997, 9(7):1483-1492
【24】 Hyvarinen A. Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Trans on Neural Networks[J], 1999, 10(3): 626-634
【25】 Cichocki A, Kasprzak W and Amari S. Multi-layer neural networks with a local adaptive learning rule for blind separation of source signals. International Symposium on Nonlinear Theory and Its Applications[C]. NTA Research Society of IEICE, Tokyo, Japan, 1995: 61-65
【26】 Cichocki A, Amari S. Self-adaptive neural networks for blind separation of sources. IEEE international symposium on circuits and systems[C]. 1996, Vol. 2:157-160
【27】 Freisleben B, Hagen C and Borschbach M. A neural network for the blind separation of non-gaussian sources. Proceedings of IEEE world congress, international joint conference on neural networks IJCNN'98[C], Anchorage IEEE Press, 1998: 837-842
【28】 Hyvarinen A and Oja E. Independent Component Analysis: Algorithms and Applications. Neural Networks[J], 2000, 13(4-5): 411-430
【29】 Bell A J and Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation[J], 1995, Vol. 7:1129-1159
【30】 Yang H H. Adaptive on-line learning algorithms for blind separation-maximum entropy and minimum mutual information. Neural Computation[J], 1997, 9: 1457-1482
【31】 Cichocki A, Thawonmas R, Amari S. Sequential blind signal extraction in order specified by stochastic properties. Electronics Letters[J], 1997, 33(1): 64-65
【32】 林秋华，殷福亮．盲源分离自适应算法的统一形式．大连理工大学学报[J]，2002，Vol．42(4)：498-503
【33】 Lee T W, Girolami M and Sejnnowski T J. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation[J], 1999, Vol. 11:417-441
【34】 Mathis H and Douglas S C. On the existence of universal nonlinearities for blind source separation. IEEE Trans On Signal Processing[J], 2002, 50(5): 1007-1016
【35】 Taro Y, Katsuhisa H and Kazuyoshi I. Independent component analysis by transforming a scatter diagram of mixtures of signals. Optics Communications[J], 2000, 173(1-6): 107-114
【36】 Mansour A, Puntonet C G and Ohnishi N. A Simple ICA algorithm based on geometrical approach, Sixth Int Symposium on Signal Processing and its Application[C]. 2001: 835-836
【37】 Mansour A, Ohnishi N, Puntonet C G. Blind multiuser separation of instantaneous mixture algorithm based on geometrical concepts. Signal processing[J], 2002, 82(8): 1155-1175
【38】 Liu D, Liu X Y, etc. Blind Source Separation Based on Dual Adaptive Control. In 4th Int. Symposium on Independent Component Analysis and Blind Signal Separation[C], Nara, Japan, 2003. 445-450
【39】 刘丁，赵艳，钱富才．一种新的联合对角化盲分离算法．中国控制与决策年会[C]，2004：231-235
【40】 刘丁，宋跃辉，王飞．基于PCA和Infomax的源信号数目未知的超定盲信号分离．2005年控制与决策年会论文集，哈尔滨，2005：538-541
【41】 Burel G. Blind separation of sources: a nonlinear neural algorithm. Neural Network,1992, Vol.5: 937-947
【42】 Pearlmuter B. and Parra L., A context-sensitive generalization of ICA. In:International Conference on Neural Information Processing, 1996:151-157
【43】 Hermann, M. and Yang, H H. Perspectives and limitations of self-organizing maps. In ICONIP'96, HongKong, 1996:129-134
【44】 Lin J. and Cowan J. Faithful representation of separable input distributions. Neural Computation, 1997:1305-1320
【45】 Yang H H, Amari S and Cichochi A. Information-theoretic approach to blind Separation of sources in non-linear mixture. Signal Processing,1998, 64(3):291-300
【46】 Taleb A and Jutten C. Nonlinear source separation: The post nonlinear mixtures. In Proceeding of Europ. Symp. On Artificial Neural Network (ESANN'97), Bruges, Belgium, 1997:79-284
【47】 Hyvarinen A and Pajunen P. Nonlinear Independent Component Analysis: Existence and Uniqueness Results. Neural Network, 1999, vol.12:429-439.
【48】 Tan Y, Wang J. and Zurada J.M. Nonlinear blind source separation using a radial basis function network. IEEE Trans. Neural Networks, 2001 Jan.,vol.12:124-134
【49】 Tan Y, and Wang, J. Nonlinear blind source separation using higher order statistics and a genetic algorithm. IEEE Trans. Evolutionary Computation, 2001 Dec., vol.5:600-612.
【50】 Liu D and Zhao Y. Nonlinear Blind Source Separation Based on Radial Basis Function. Proc of the 5th World Congress on Intelligent Control and Automation[C]. Hangzhou, P.R. China, 2004: 2002-2004
【51】 张贤达，保铮．通信信号处理[M]．北京：国防工业出版社，2000
【52】 杨行峻，郑君里．人工神经网络与盲信号处理[M]．北京：清华大学出版社，2003
【53】 徐明彪，朱维彰．关于信号盲分离分离效果评判指标的分析．杭州电子工业学院学报[J]．2002，Vol．22(3)：63-66
【54】 朱孝龙，张贤达．基于奇异值分解的超定盲信号分离[J]．电子与信息学报，2004，vol．26(3)：337-343
【55】 Andrzej Cichocki and Tomasz Rutkowski et al, "A Blind Extration of Temporally Correlated But Statistically Dependent Acoustic Signals" accept by NNSP'2000.
【56】 Andrzej Cichocki, Thawonmas and Shun-ichi Amari, "On-Line Blind Signal Extraction Methods Exploiting a Priori Knowledge of the Previously Extracted Signals", in Proc. IEEE Workshop on Nonlinear Signal and Image Processing, Michigan, 1997,vol.4:2135-2140
【57】 Tetsuya, Allan Kardec Barros, Tomasz Rutkowski and Andrzej Cichocki, "Speech Extraction Based upon a Combined Subband Independent Component Analysis and Neural Memory", 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), April 2003, Nara, Japan, 355-360.
【58】 TI. TMS320DM642 Evaluation Module quick start installation guide. 2003.
【59】 TI. TMS320DM642 Video/Imaging Fixed-Point Digital Signal Processor data manual. 2002.
【60】 TI. TMS320C64x DSP Two-Level Internal Memory Reference Guide. 2003.
【61】 TI. TMS320DM642 Evaluation Module Technical Reference. 2003.
【62】 尹勇，欧光军，关荣锋．DSP集成开发环境CCS使用指南．北京：北京航空航天大学出社．2003．
【63】 TI. TLV320AIC23 Stereo Audio Codec Data Manual. 2002.
【64】 TI. TMS320C6000 DSP Multi channel Audio Serial Port (McASP) Reference Guide. 2003.
【2】 Cardoso J E Blind Signal separation: statistical principles. Proc of the IEEE[J], 1998, Vol.86(10): 2009-2025
【3】 Amari S I, Cichocki A and Yang H H. Blind Signal Separation and Extraction: Neural and Information-theoretic approaches. In: S. Haykin, editor, Unsupervised Adaptive Filtering [C]. New York: John Wiley & Sons, 2000, Vol.1: 103-104
【4】 Hyvarinen A, Karhunen J, Oja E. Independent Component Analysis [M]. New York: John Wiley & Sons, 2001
【5】 Cichocki A，Amari S等著．自适应盲信号与图像处理[M]．吴正国等译．北京：电子工业出版社，2005．5
【6】 余英林，谢胜利，蔡汉添等．信号处理新方法导论[M]．北京：清华大学出版社，2004
【7】 Tan Y and Wang J. Nonlinear Blind Source Separation Using Higher Order Statistics and a Genetic Algorithm. IEEE Trans On Evolutionary Computation[J], 2001, Vol.5(6): 600-612
【8】 章晋龙，何昭水，谢胜利．基于遗传算法的有序盲信号提取．电子学报[J]．2004，Vol．32(4)：616-619
【9】 Cao X R, Liu R W. A general approach to blind source separation [J]. IEEE Trans. Signal Processing, 1996, Vol. 44(3): 562-571.
【10】 Cichocki A, Thawonmas R, Amari S. Sequential blind signal extraction in order specified by stochastic properties[J]. Electronics Letters, 1997, Vol. 33(1, 2): 64-65
【11】 M. Dorigo, L M. Gambardella. Ant colony system: A cooperative learning approach to the traveling salesman problem[J], IEEE Trans on Evalutionary Computering, 1997, Vol .1(1):53-56.
【12】 李士勇等编著 蚁群算法及其应用 哈尔滨：哈尔滨工业大学出版社，2004．9
【13】 Ding LIU, Fei WANG. BSSLAB:A Toolbox for Blind Signal Separation and Independent Component Analysis. 2005 International Workshop on SCILAB and Open Source Software Engineering, Wuhan, P.R.China, 2005:77-83
【14】 TI.TMS320C6000 CPU and Instruction Set Reference Guide.2003.
【15】 苏涛等编著．DSP实用技术．西安：西安电子科技大学出版社．2002．
【16】 TMS320C64x RTDX Help (SPRH053). Texas Instruments, 1998
【17】 顾长怡，黄佩伟．利用RTDX技术实现计算机实时话音分析计算机工程[J] 2001，Vol．27(2)：155-156
【18】 TI. TMS320DM642 Evaluation Module quick start installation guide. 2003
【19】 Jutten C, Herault J., Blind separation of sources, Part Ⅰ: An adaptive algorithmbased on neuromimetic. Signal Processing[J], 1991, 24(1):1-10
【20】 Cardoso J F and Souloumiac A., Blind beam forming for non Gaussian signals. In IEE Proceedings-F[J], 1993, 140(6): 362-370
【21】 Comon P. Independent component analysis-a new concept. Signal Processing[J], 1994,36(3): 287-314
【22】 Puntonet C G, Pdeto A, Ortega J. New geometrical approach for blind separation of sources mapped to a neural network. International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing[C], 1996:174-182
【23】 Hyvarinen A and Oja E. A Fast Fixed-Point Algorithm for Independent Component Analysis. Neural Computation[J], 1997, 9(7):1483-1492
【24】 Hyvarinen A. Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Trans on Neural Networks[J], 1999, 10(3): 626-634
【25】 Cichocki A, Kasprzak W and Amari S. Multi-layer neural networks with a local adaptive learning rule for blind separation of source signals. International Symposium on Nonlinear Theory and Its Applications[C]. NTA Research Society of IEICE, Tokyo, Japan, 1995: 61-65
【26】 Cichocki A, Amari S. Self-adaptive neural networks for blind separation of sources. IEEE international symposium on circuits and systems[C]. 1996, Vol. 2:157-160
【27】 Freisleben B, Hagen C and Borschbach M. A neural network for the blind separation of non-gaussian sources. Proceedings of IEEE world congress, international joint conference on neural networks IJCNN'98[C], Anchorage IEEE Press, 1998: 837-842
【28】 Hyvarinen A and Oja E. Independent Component Analysis: Algorithms and Applications. Neural Networks[J], 2000, 13(4-5): 411-430
【29】 Bell A J and Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation[J], 1995, Vol. 7:1129-1159
【30】 Yang H H. Adaptive on-line learning algorithms for blind separation-maximum entropy and minimum mutual information. Neural Computation[J], 1997, 9: 1457-1482
【31】 Cichocki A, Thawonmas R, Amari S. Sequential blind signal extraction in order specified by stochastic properties. Electronics Letters[J], 1997, 33(1): 64-65
【32】 林秋华，殷福亮．盲源分离自适应算法的统一形式．大连理工大学学报[J]，2002，Vol．42(4)：498-503
【33】 Lee T W, Girolami M and Sejnnowski T J. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation[J], 1999, Vol. 11:417-441
【34】 Mathis H and Douglas S C. On the existence of universal nonlinearities for blind source separation. IEEE Trans On Signal Processing[J], 2002, 50(5): 1007-1016
【35】 Taro Y, Katsuhisa H and Kazuyoshi I. Independent component analysis by transforming a scatter diagram of mixtures of signals. Optics Communications[J], 2000, 173(1-6): 107-114
【36】 Mansour A, Puntonet C G and Ohnishi N. A Simple ICA algorithm based on geometrical approach, Sixth Int Symposium on Signal Processing and its Application[C]. 2001: 835-836
【37】 Mansour A, Ohnishi N, Puntonet C G. Blind multiuser separation of instantaneous mixture algorithm based on geometrical concepts. Signal processing[J], 2002, 82(8): 1155-1175
【38】 Liu D, Liu X Y, etc. Blind Source Separation Based on Dual Adaptive Control. In 4th Int. Symposium on Independent Component Analysis and Blind Signal Separation[C], Nara, Japan, 2003. 445-450
【39】 刘丁，赵艳，钱富才．一种新的联合对角化盲分离算法．中国控制与决策年会[C]，2004：231-235
【40】 刘丁，宋跃辉，王飞．基于PCA和Infomax的源信号数目未知的超定盲信号分离．2005年控制与决策年会论文集，哈尔滨，2005：538-541
【41】 Burel G. Blind separation of sources: a nonlinear neural algorithm. Neural Network,1992, Vol.5: 937-947
【42】 Pearlmuter B. and Parra L., A context-sensitive generalization of ICA. In:International Conference on Neural Information Processing, 1996:151-157
【43】 Hermann, M. and Yang, H H. Perspectives and limitations of self-organizing maps. In ICONIP'96, HongKong, 1996:129-134
【44】 Lin J. and Cowan J. Faithful representation of separable input distributions. Neural Computation, 1997:1305-1320
【45】 Yang H H, Amari S and Cichochi A. Information-theoretic approach to blind Separation of sources in non-linear mixture. Signal Processing,1998, 64(3):291-300
【46】 Taleb A and Jutten C. Nonlinear source separation: The post nonlinear mixtures. In Proceeding of Europ. Symp. On Artificial Neural Network (ESANN'97), Bruges, Belgium, 1997:79-284
【47】 Hyvarinen A and Pajunen P. Nonlinear Independent Component Analysis: Existence and Uniqueness Results. Neural Network, 1999, vol.12:429-439.
【48】 Tan Y, Wang J. and Zurada J.M. Nonlinear blind source separation using a radial basis function network. IEEE Trans. Neural Networks, 2001 Jan.,vol.12:124-134
【49】 Tan Y, and Wang, J. Nonlinear blind source separation using higher order statistics and a genetic algorithm. IEEE Trans. Evolutionary Computation, 2001 Dec., vol.5:600-612.
【50】 Liu D and Zhao Y. Nonlinear Blind Source Separation Based on Radial Basis Function. Proc of the 5th World Congress on Intelligent Control and Automation[C]. Hangzhou, P.R. China, 2004: 2002-2004
【51】 张贤达，保铮．通信信号处理[M]．北京：国防工业出版社，2000
【52】 杨行峻，郑君里．人工神经网络与盲信号处理[M]．北京：清华大学出版社，2003
【53】 徐明彪，朱维彰．关于信号盲分离分离效果评判指标的分析．杭州电子工业学院学报[J]．2002，Vol．22(3)：63-66
【54】 朱孝龙，张贤达．基于奇异值分解的超定盲信号分离[J]．电子与信息学报，2004，vol．26(3)：337-343
【55】 Andrzej Cichocki and Tomasz Rutkowski et al, "A Blind Extration of Temporally Correlated But Statistically Dependent Acoustic Signals" accept by NNSP'2000.
【56】 Andrzej Cichocki, Thawonmas and Shun-ichi Amari, "On-Line Blind Signal Extraction Methods Exploiting a Priori Knowledge of the Previously Extracted Signals", in Proc. IEEE Workshop on Nonlinear Signal and Image Processing, Michigan, 1997,vol.4:2135-2140
【57】 Tetsuya, Allan Kardec Barros, Tomasz Rutkowski and Andrzej Cichocki, "Speech Extraction Based upon a Combined Subband Independent Component Analysis and Neural Memory", 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), April 2003, Nara, Japan, 355-360.
【58】 TI. TMS320DM642 Evaluation Module quick start installation guide. 2003.
【59】 TI. TMS320DM642 Video/Imaging Fixed-Point Digital Signal Processor data manual. 2002.
【60】 TI. TMS320C64x DSP Two-Level Internal Memory Reference Guide. 2003.
【61】 TI. TMS320DM642 Evaluation Module Technical Reference. 2003.
【62】 尹勇，欧光军，关荣锋．DSP集成开发环境CCS使用指南．北京：北京航空航天大学出社．2003．
【63】 TI. TLV320AIC23 Stereo Audio Codec Data Manual. 2002.
【64】 TI. TMS320C6000 DSP Multi channel Audio Serial Port (McASP) Reference Guide. 2003.