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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 93 (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 74 (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 35 (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 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
Springer Handbook on Speech Processing and Speech Communication 1 A SURVEY OF CONVOLUTIVE BLIND SOURCE SEPARATION METHODS
"... 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 1 (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. 1.
Polynomial Matrix Whitening And Application To The Multichannel Blind Deconvolution Problem
 in MILCOM’95
, 1995
"... A method for whitening a polynomial matrix is described, including the calculation of the eigenvalue polynomials and eigenvector polynomials of an FIR polynomial matrix. The multichannel blind deconvolution problem is briefly described and FIR polynomial matrix whitening is applied to the problem. B ..."
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Cited by 1 (0 self)
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A method for whitening a polynomial matrix is described, including the calculation of the eigenvalue polynomials and eigenvector polynomials of an FIR polynomial matrix. The multichannel blind deconvolution problem is briefly described and FIR polynomial matrix whitening is applied to the problem. Benefits of the whitening technique are demonstrated through simulation. Data prewhitening or the use of an exact least squares adaptation is necessary in any problem of moderate complexity. The group theoretic aspects of FIR polynomial matrix algebra are discussed. 1. INTRODUCTION AND MOTIVATION A method for whitening a multichannel linear system is presented. Multiple input and multiple output linear systems are considered. A two input and two output system would be written as H = h 11 h 21 h 12 h 22 : (1) The h ij 's are FIR filters which each represent an acoustic multipath transfer function from source i to sensor j. Referring to Figure 1, a twosensor, twosource problem can...