Results 1  10
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37
Independent Component Analysis
 Neural Computing Surveys
, 2001
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
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Cited by 1493 (93 self)
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A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Wellknown linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA. 1
An informationmaximization approach to blind separation and blind deconvolution
 NEURAL COMPUTATION
, 1995
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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 73 (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...
Blind Separation Of Delayed Sources Based On Information Maximization
, 1996
"... Recently, Bell and Sejnowski have presented an approach to blind source separation based on the information maximization principle. We extend this approach into more general cases where the sources may have been delayed with respect to each other. We present a network architecture capable of coping ..."
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Cited by 32 (1 self)
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Recently, Bell and Sejnowski have presented an approach to blind source separation based on the information maximization principle. We extend this approach into more general cases where the sources may have been delayed with respect to each other. We present a network architecture capable of coping with such sources, and we derive the adaptation equations for the delays and the weights in the network by maximizing the information transferred through the network. Examples using wideband sources such as speech are presented to illustrate the algorithm.
Independent Component Analysis, A Survey Of Some Algebraic Methods
 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS
, 1996
"... The source separation problem has been addressed in many ways during the last decade, and one of its instances gave birth to Independent Component Analysis (ICA). Iterative methods can be opposed to algebraic ones for the computation of the ICA, and seem to reveal very interesting research tracks. T ..."
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Cited by 25 (0 self)
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The source separation problem has been addressed in many ways during the last decade, and one of its instances gave birth to Independent Component Analysis (ICA). Iterative methods can be opposed to algebraic ones for the computation of the ICA, and seem to reveal very interesting research tracks. This paper attempts to give an outline of some of the works that have been carried out in the latter area, without pretending to survey exhaustively or objectively the subject. Bibliographical pointers hopefully compensate for this drawback.
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.
Multichannel Blind Signal Separation And Reconstruction
 IEEE Trans. Speech Audio Processing
, 1997
"... Separation of multiple signals from their superposition recorded at several sensors is addressed. The methods employ polyspectra of the sensor data in order to extract the unknown signals and estimate the finite impulse response (FIR) coupling systems via a linear equation based algorithm. The proce ..."
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Cited by 19 (0 self)
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Separation of multiple signals from their superposition recorded at several sensors is addressed. The methods employ polyspectra of the sensor data in order to extract the unknown signals and estimate the finite impulse response (FIR) coupling systems via a linear equation based algorithm. The procedure is useful for multichannel blind deconvolution of colored input signals with (possibly) overlapping spectra. An extension of the main algorithm, which can be applied for quasiperiodic signal separation, is also given. Simulation results corroborate the applicability of the algorithm.
A globally convergent approach for blind MIMO adaptive deconvolution
 IEEE Trans. Signal Processing,vol.49,no.6,pp
"... Abstract—We discuss the blind deconvolution of multiple input/multiple output (MIMO) linear convolutional mixtures and propose a set of hierarchical criteria motivated by the maximum entropy principle. The proposed criteria are based on the constant–modulus (CM) criterion in order to guarantee that ..."
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Cited by 19 (0 self)
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Abstract—We discuss the blind deconvolution of multiple input/multiple output (MIMO) linear convolutional mixtures and propose a set of hierarchical criteria motivated by the maximum entropy principle. The proposed criteria are based on the constant–modulus (CM) criterion in order to guarantee that all minima achieve perfectly restoration of different sources. The approach is moreover robust to errors in channel order estimation. Practical implementation is addressed by a stochastic adaptive algorithm with a low computational cost. Complete convergence proofs, based on the characterization of all extrema, are provided. The efficiency of the proposed method is illustrated by numerical simulations. Index Terms—Blind adaptive source separation, constant modulus criterion, multiple input/multiple output convolutional systems. I.
Adaptive blind source separation and equalization for multipleinput/multipleoutput systems
 IEEE Transactions on Information Theory
"... Abstract—In this paper, we investigate adaptive blind source separation and equalization for multipleinput/multipleoutput (MIMO) systems. We first analyze the convergence of the constant modulus algorithm (CMA) used in MIMO systems (MIMOCMA). Our analysis reveals that the MIMOCMA equalizer is ab ..."
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Cited by 12 (2 self)
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Abstract—In this paper, we investigate adaptive blind source separation and equalization for multipleinput/multipleoutput (MIMO) systems. We first analyze the convergence of the constant modulus algorithm (CMA) used in MIMO systems (MIMOCMA). Our analysis reveals that the MIMOCMA equalizer is able to recover one of the input signals, remove the intersymbol interference (ISI), and suppress the other input signals. Furthermore, for the MIMO finite impulse response (FIR) systems satisfying certain conditions, the MIMOCMA FIR equalizers are able to perfectly recover one of the system inputs regardless of the initial settings. We then propose a novel algorithm for blind source separation and equalization for MIMO systems. Our theoretical analysis proves that the new blind algorithm is able to recover all system inputs simultaneously regardless of the initial settings. Finally, computer simulation examples are presented to confirm our analysis and illustrate the effectiveness of blind source separation and equalization for MIMO systems. Index Terms—Blind equalization, convergence, multipleinput/ multipleoutput system, source separation. I.