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133
Frequencydomain blind source separation of many speech signals using nearfield and farfield models
 EURASIP Journal on Applied Signal Processing, 2006:Article ID 83683
, 2006
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Audio source separation of convolutive mixtures
 IEEE Trans. Speech Audio Process
, 2003
"... Abstract — The problem of separation of audio sources recorded in a real world situation is well established in modern literature. A method to solve this problem is Blind Source Separation (BSS) using Independent Component Analysis (ICA). The recording environment is usually modelled as convolutive. ..."
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Cited by 41 (7 self)
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Abstract — The problem of separation of audio sources recorded in a real world situation is well established in modern literature. A method to solve this problem is Blind Source Separation (BSS) using Independent Component Analysis (ICA). The recording environment is usually modelled as convolutive. Previous research on ICA of instantaneous mixtures provided solid background for the separation of convolved mixtures. The authors revise current approaches on the subject and propose a fast frequency domain ICA framework, providing a solution for the apparent permutation problem encountered in these methods.
Independent component analysis by complex nonlinearities
 in Proc. ICASSP
, 2004
"... A number of complex nonlinear functions are proposed for the independent component analysis (ICA) of complexvalued data. We discuss the properties of these nonlinearities and show their efficiency in generating the higher order statistics needed for ICA. 1. ..."
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Cited by 30 (14 self)
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A number of complex nonlinear functions are proposed for the independent component analysis (ICA) of complexvalued data. We discuss the properties of these nonlinearities and show their efficiency in generating the higher order statistics needed for ICA. 1.
Blind identification of overcomplete mixtures of sources
 BIOME)”, Lin. Algebra Appl
, 2004
"... The problem of Blind Identification of linear mixtures of independent random processes is known to be related to the diagonalization of some tensors. This problem is posed here in terms of a non conventional joint approximate diagonalization of several matrices. In fact, a congruent transform is app ..."
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Cited by 23 (15 self)
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The problem of Blind Identification of linear mixtures of independent random processes is known to be related to the diagonalization of some tensors. This problem is posed here in terms of a non conventional joint approximate diagonalization of several matrices. In fact, a congruent transform is applied to each of these matrices, the left transform being rectangular full rank, and the right one being unitary. The application in antenna signal processing is described, and suboptimal numerical algorithms are proposed.
Complex independent component analysis by entropy bound minimization
 IEEE Trans. on Circuits and Systems I
"... Abstract—We first present a new (differential) entropy estimator for complex random variables by approximating the entropy estimate using a numerically computed maximum entropy bound. The associated maximum entropy distributions belong to the class of weighted linear combinations and elliptical dist ..."
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Cited by 21 (10 self)
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Abstract—We first present a new (differential) entropy estimator for complex random variables by approximating the entropy estimate using a numerically computed maximum entropy bound. The associated maximum entropy distributions belong to the class of weighted linear combinations and elliptical distributions, and together, they provide a rich array of bivariate distributions for density matching. Next, we introduce a new complex independent component analysis (ICA) algorithm, complex ICA by entropybound minimization (complex ICAEBM), using this new entropy estimator and a line search optimization procedure. We present simulation results to demonstrate the superior separation performance and computational efficiency of complex ICAEBM in separation of complex sources that come from a wide range of bivariate distributions. Index Terms—Complex optimization, complex random variable, differential entropy, independent component analysis (ICA), neural networks, principle of maximum entropy. I.
Complex ICA using nonlinear functions
 IEEE Trans. Signal Process
, 2008
"... Abstract—We introduce a framework based on Wirtinger calculus for nonlinear complexvalued signal processing such that all computations can be directly carried out in the complex domain. The two main approaches for performing independent component analysis, maximum likelihood, and maximization of no ..."
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Cited by 20 (12 self)
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Abstract—We introduce a framework based on Wirtinger calculus for nonlinear complexvalued signal processing such that all computations can be directly carried out in the complex domain. The two main approaches for performing independent component analysis, maximum likelihood, and maximization of nonGaussianity—which are intimately related to each other—are studied using this framework. The main update rules for the two approaches are derived, their properties and density matching strategies are discussed along with numerical examples to highlight their relationships. Index Terms—Complex optimization, density matching, independent component analysis (ICA), maximumlikelihood estimation, negentropy maximization. I.
ComplexValued Signal Processing: The Proper Way to Deal With Impropriety
"... Abstract—Complexvalued signals occur in many areas of ..."
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Cited by 19 (1 self)
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Abstract—Complexvalued signals occur in many areas of
Adaptable nonlinearity for complex maximization of nongaussianity and a fixedpoint algorithm
 in Proc. IEEE Workshop on Machine Learning for Signal Processing (MLSP
, 2006
"... Complex maximization of nongaussianity (CMN) has been shown to provide reliable separation of both circular and noncircular sources using a class of complex functions in the nonlinearity. In this paper, we derive a fixedpoint algorithm for blind separation of noncircular sources using CMN. We also ..."
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Cited by 19 (12 self)
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Complex maximization of nongaussianity (CMN) has been shown to provide reliable separation of both circular and noncircular sources using a class of complex functions in the nonlinearity. In this paper, we derive a fixedpoint algorithm for blind separation of noncircular sources using CMN. We also introduce the adaptive CMN (ACMN) algorithm that provides significant performance improvement by adapting the nonlinearity to the source distribution. The ability of ACMN to adapt to a wide range of source statistics is demonstrated by simulation results. 1.
The maximum likelihood approach to complex ICA
 in Proc. ICASSP
, 2006
"... We derive the form of the best nonlinear functions for performing independent component analysis (ICA) by maximum likelihood estimation. We show that both the form of nonlinearity and the relative gradient update equations for likelihood maximization naturally generalize to the complex case, and th ..."
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Cited by 18 (11 self)
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We derive the form of the best nonlinear functions for performing independent component analysis (ICA) by maximum likelihood estimation. We show that both the form of nonlinearity and the relative gradient update equations for likelihood maximization naturally generalize to the complex case, and that they coincide with the real case. We discuss several special cases for the score function as well as adaptive scores. 1.