Results 1  10
of
97
Blind Signal Separation: Statistical Principles
, 2003
"... Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mut ..."
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Cited by 522 (4 self)
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Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach but requires to venture beyond familiar second order statistics. The objective of this paper is to review some of the approaches that have been recently developed to address this exciting problem, to show how they stem from basic principles and how they relate to each other.
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 89 (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...
ICA Using Spacings Estimates of Entropy
 Journal of Machine Learning Research
, 2003
"... This paper presents a new algorithm for the independent components analysis (ICA) problem based on an efficient entropy estimator. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated KullbackLeibler divergence betwee ..."
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Cited by 68 (3 self)
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This paper presents a new algorithm for the independent components analysis (ICA) problem based on an efficient entropy estimator. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated KullbackLeibler divergence between the joint distribution and the product of the marginal distributions. We pair this approach with efficient entropy estimators from the statistics literature. In particular, the entropy estimator we use is consistent and exhibits rapid convergence. The algorithm based on this estimator is simple, computationally efficient, intuitively appealing, and outperforms other well known algorithms. In addition, the estimator's relative insensitivity to outliers translates into superior performance by our ICA algorithm on outlier tests. We present favorable comparisons to the Kernel ICA, FASTICA, JADE, and extended Infomax algorithms in extensive simulations. We also provide public domain source code for our algorithms.
Adaptive blind signal processingneural network approaches
 Proc. of the IEEE
, 1998
"... Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss recent developments of adaptive learnin ..."
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Cited by 61 (9 self)
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Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss recent developments of adaptive learning algorithms based on the natural gradient approach and their properties concerning convergence, stability, and efficiency. Several promising schemas are proposed and reviewed in the paper. Emphasis is given to neural networks or adaptive filtering models and associated online adaptive nonlinear learning algorithms. Computer simulations illustrate the performances of the developed algorithms. Some results presented in this paper are new and are being published for the first time.
Recent Developments in Blind Channel Equalization: From Cyclostationarity to Subspaces
 Signal Processing
, 1996
"... Since Tong, Xu and Kailath [1] demonstrated the feasibility of identifying possibly nonminimum phase channels using secondorder statistics, considerable research activity, both in algorithm development and fundamental analysis, has been seen in the area of blind identification of multiple FIR chan ..."
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Cited by 49 (1 self)
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Since Tong, Xu and Kailath [1] demonstrated the feasibility of identifying possibly nonminimum phase channels using secondorder statistics, considerable research activity, both in algorithm development and fundamental analysis, has been seen in the area of blind identification of multiple FIR channels. Many of the recently developed approaches invoke, either explicitly or implicitly, the algebraic structure of the data model, while some others resort to the use of cyclic correlation/spectral fitting techniques. The objective of this paper is to establish insightful connections among these studies and present recent developments of blind channel equalization. We also unify various representative algorithms into a common theoretical framework. 1 1
Blind System Identification
, 1997
"... Blind system identification is a fundamental signal processing technology aimed to retrieve unknown information of a system from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation and blind image restoration. Th ..."
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Cited by 46 (3 self)
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Blind system identification is a fundamental signal processing technology aimed to retrieve unknown information of a system from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation and blind image restoration. This paper reviews a number of recently developed concepts and techniques for blind system identification which include the concept of blind system identifiability in a deterministic framework, the blind techniques of maximum likelihood and subspace for estimating the system's impulse response, and other techniques for direct estimation of the system input. Keywords: System identification, Blind techniques, Multichannels, Equalization, Source separation. This work has been supported by the Australian Research Council and the Australian Cooperative Research Center for Sensor Signal and Information Processing. y Currently with Motorola Australian Research Centre, 12 Lord Street, Botany 2019, ...
Online blind multichannel equalization based on mut ually referenced filters
 IEEE Trans. Signal Processing
, 1997
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Generalized correlation function: Definition, properties and application to blind equalization
 IEEE Transactions on Signal Processing
, 2006
"... Abstract—With an abundance of tools based on kernel methods and information theoretic learning, a void still exists in incorporating both the time structure and the statistical distribution of the time series in the same functional measure. In this paper, a new generalized correlation measure is de ..."
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Cited by 40 (9 self)
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Abstract—With an abundance of tools based on kernel methods and information theoretic learning, a void still exists in incorporating both the time structure and the statistical distribution of the time series in the same functional measure. In this paper, a new generalized correlation measure is developed that includes the information of both the distribution and that of the time structure of a stochastic process. It is shown how this measure can be interpreted from a kernel method as well as from an information theoretic learning points of view, demonstrating some relevant properties. To underscore the effectiveness of the new measure, a simple blind equalization problem is considered using a coded signal. Index Terms—Blind equalization, entropy, generalized correlation kernel, information theoretic learning, reproducing kernel Hilbert space (RKHS). I.
OnLine Entropy Manipulation: Stochastic Information Gradient
 IEEE Signal Processing Letters
, 2003
"... Abstract—Entropy has found significant applications in numerous signal processing problems including independent components analysis and blind deconvolution. In general, entropy estimators require ( 2) operations, being the number of samples. For practical online entropy manipulation, it is desirabl ..."
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Cited by 22 (12 self)
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Abstract—Entropy has found significant applications in numerous signal processing problems including independent components analysis and blind deconvolution. In general, entropy estimators require ( 2) operations, being the number of samples. For practical online entropy manipulation, it is desirable to determine a stochastic gradient for entropy, which has ( ) complexity. In this letter, we propose a stochastic Shannon’s entropy estimator. We determine the corresponding stochastic gradient and investigate its performance. The proposed stochastic gradient for Shannon’s entropy can be used in online adaptation problems where the optimization of an entropybased cost function is necessary. Index Terms—Shannon’s entropy, stochastic gradient for entropy. I.
Multichannel blind deconvolution of nonminimum phase systems using information backpropagation
 in: Proceedings of the Fifth International Conference on Neural Information Processing (ICONIP’99
, 1999
"... Abstract—In this paper, we present a new filter decomposition method for multichannel blind deconvolution of nonminimumphase systems. With this approach, we decompose a doubly finite impulse response filter into a cascade form of two filters: a causal finite impulse response (FIR) filter and an an ..."
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Cited by 21 (13 self)
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Abstract—In this paper, we present a new filter decomposition method for multichannel blind deconvolution of nonminimumphase systems. With this approach, we decompose a doubly finite impulse response filter into a cascade form of two filters: a causal finite impulse response (FIR) filter and an anticausal FIR filter. After introducing a Lie group to the manifold of FIR filters, we discuss geometric properties of the FIR filter manifold. Using the nonholonomic transform, we derive the natural gradient on the FIR manifold. By simplifying the mutual information rate, we present a very simple cost function for blind deconvolution of nonminimumphase systems. Subsequently, the natural gradient algorithms are developed both for the causal FIR filter and for the anticausal FIR filter. Simulations are presented to illustrate the validity and favorable learning performance of the proposed algorithms. Index Terms—Blind deconvolution, independent component analysis, natural gradient, nonmimimumphase systems. I.