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Blind Separation Of Convolved Sources Based On Information Maximization
 IN IEEE WORKSHOP ON NEURAL NETWORKS FOR SIGNAL PROCESSING
, 1996
"... Blind separation of independent sources from their convolutive mixtures is a problem in many real world multisensor applications. In this paper we present a solution to this problem based on the information maximization principle, which was recently proposed by Bell and Sejnowski for the case of bl ..."
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Cited by 98 (1 self)
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Blind separation of independent sources from their convolutive mixtures is a problem in many real world multisensor applications. In this paper we present a solution to this problem based on the information maximization principle, which was recently proposed by Bell and Sejnowski for the case of blind separation of instantaneous mixtures. We present a feedback network architecture capable of coping with convolutive mixtures, and we derive the adaptation equations for the adaptive filters in the network by maximizing the information transferred through the network. Examples using speech signals are presented to illustrate the algorithm.
Multichannel Blind Deconvolution: Fir Matrix Algebra And Separation Of Multipath Mixtures
, 1996
"... A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and mat ..."
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Cited by 77 (0 self)
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A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and matrix algorithms for use in multichannel /multipath problems. Using abstract algebra/group theoretic concepts, information theoretic principles, and the Bussgang property, methods of single channel filtering and source separation of multipath mixtures are merged into a general FIR matrix framework. Techniques developed for equalization may be applied to source separation and vice versa. Potential applications of these results lie in neural networks with feedforward memory connections, wideband array processing, and in problems with a multiinput, multioutput network having channels between each source and sensor, such as source separation. Particular applications of FIR polynomial matrix alg...
Survey of Sparse and NonSparse Methods in Source Separation
, 2005
"... Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sour ..."
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Cited by 41 (1 self)
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Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources. When the information about the mixing process and sources is limited, the problem is called ‘blind’. By assuming that the sources can be represented sparsely in a given basis, recent research has demonstrated that solutions to previously problematic blind source separation problems can be obtained. In some cases, solutions are possible to problems intractable by previous nonsparse methods. Indeed, sparse methods provide a powerful approach to the separation of linear mixtures of independent data. This paper surveys the recent arrival of sparse blind source separation methods and the previously existing nonsparse methods, providing insights and appropriate hooks into the literature along the way.
A SURVEY OF CONVOLUTIVE BLIND SOURCE SEPARATION METHODS
 SPRINGER HANDBOOK ON SPEECH PROCESSING AND SPEECH COMMUNICATION
"... In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to realworld audio ..."
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Cited by 23 (0 self)
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In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to realworld audio separation tasks.
Source Separation Using Information Measures in the Time and Frequency Domains
, 1999
"... First and foremost I wish to thank my advisor, Dr. José Principe, for being my advisor and for his inspiration and support through my Ph. D. study. Without his thoughtprovoking guidance and neverending encouragement, this dissertation would not have been possible. I also wish to thank the members ..."
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Cited by 8 (2 self)
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First and foremost I wish to thank my advisor, Dr. José Principe, for being my advisor and for his inspiration and support through my Ph. D. study. Without his thoughtprovoking guidance and neverending encouragement, this dissertation would not have been possible. I also wish to thank the members of my committee, Dr. Fredrick Taylor, Dr. John Harris, Dr. William Edmonson, and Dr. Howard Rothman, for their invaluable time and interest in serving on my supervisory committee, as well as their insightful comments which improved the quality of this dissertation. Special thanks go out to all the former and current CNEL colleagues. Especially I would like to express my immense gratitude to Dr. Dongxin Xu, Dr. YuMing Chiang and Dr.tobe ShaoJen Lim for their friendship and their offering stimulating discussions during the course of my Ph. D. research. Last but not least, I wish to thank my parents, brother and sister for their ceaseless love and firm support and for instilling in me a love of learning. I would like to thank my wife, HuiChen, for enduring a seemingly endless ordeal, for sacrificing some of her best years so that I could finally finish this Ph. D. research.
Source Separation Based on Second Order Statistics  an Algebraic Approach
 In Proceedings of the VIII European Signal Processing Conference
, 1996
"... this paper is a blockmethod based on secondorder statistics of the measurement data only. The parameters of the inverse filter are to be found such that the resulting filtered output signals y 1 (t) and y 2 (t) have zero crosscovariance function. Assuming a certain filter structure, the resulting ..."
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Cited by 4 (3 self)
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this paper is a blockmethod based on secondorder statistics of the measurement data only. The parameters of the inverse filter are to be found such that the resulting filtered output signals y 1 (t) and y 2 (t) have zero crosscovariance function. Assuming a certain filter structure, the resulting conditions take the form of bilinear equations. The usual approach at this point is to set up a cost
Polynomial matrix whitening and application to the multichannel blind deconvolution problem
 IEEE Conference on Military Communications
"... ..."
Novel Quadratic Gaussianity Measures and their Application in Blind Source Separation/Extraction
"... Various existing criteria to characterize the statistical independence are applied in blind source separation and independent component analysis. However, almost all of them are based on parametric models. The distribution model mismatch between the output PDF (Probability Density Functions) and the ..."
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Various existing criteria to characterize the statistical independence are applied in blind source separation and independent component analysis. However, almost all of them are based on parametric models. The distribution model mismatch between the output PDF (Probability Density Functions) and the chosen underlying distribution model is a serious problem in blind signal processing. Nonparametric PDF estimates like the Parzen window applied to the popular Kullback Leibler divergence produce computational difficulties. Hence we propose a new measure, the Quadratic Gaussianity Measure, which is associated with the Euclidean distance between the marginal probability density function and the Gaussian distribution. We show that it outperforms other Gaussianity measures in signal processing applications, such as standardized kurtosis tests because our novel Gaussianity measure is robust to changes in the distribution form. Introduction Information theory has been widely applied in commu...
GENERALIZED ANTIHEBBIAN LEARNING FOR SOURCE SEPARATION
"... The informationtheoretic framework for source separation is highly suitable. However the choice of the nonlinearity or the estimation of the multidimensional joint probability density function are nontrivial. We propose here a generalized Gaussian model to construct a generalized blind source separ ..."
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The informationtheoretic framework for source separation is highly suitable. However the choice of the nonlinearity or the estimation of the multidimensional joint probability density function are nontrivial. We propose here a generalized Gaussian model to construct a generalized blind source separation network based on the minimum entropy principle. This new separation network can suppress the interference to a significant amount compared to the traditional LMSechocanceler. The simulation is given to show the disparity of the performance as α varies. Finally how to choose the appropriate α in our generalized antiHebbian rule is discussed. 1.