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A Unifying Informationtheoretic Framework for Independent Component Analysis
, 1999
"... We show that different theories recently proposed for Independent Component Analysis (ICA) lead to the same iterative learning algorithm for blind separation of mixed independent sources. We review those theories and suggest that information theory can be used to unify several lines of research. Pea ..."
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Cited by 82 (8 self)
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We show that different theories recently proposed for Independent Component Analysis (ICA) lead to the same iterative learning algorithm for blind separation of mixed independent sources. We review those theories and suggest that information theory can be used to unify several lines of research. Pearlmutter and Parra (1996) and Cardoso (1997) showed that the infomax approach of Bell and Sejnowski (1995) and the maximum likelihood estimation approach are equivalent. We show that negentropy maximization also has equivalent properties and therefore all three approaches yield the same learning rule for a fixed nonlinearity. Girolami and Fyfe (1997a) have shown that the nonlinear Principal Component Analysis (PCA) algorithm of Karhunen and Joutsensalo (1994) and Oja (1997) can also be viewed from informationtheoretic principles since it minimizes the sum of squares of the fourthorder marginal cumulants and therefore approximately minimizes the mutual information (Comon, 1994). Lambert (19...
Blind Separation of Delayed and Convolved Sources
, 1997
"... We address the difficult problem of separating multiple speakers with multiple microphones in a real room. We combine the work of Torkkola and Amari, Cichocki and Yang, to give Natural Gradient information maximisation rules for recurrent (IIR) networks, blindly adjusting delays, separating and deco ..."
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Cited by 63 (1 self)
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We address the difficult problem of separating multiple speakers with multiple microphones in a real room. We combine the work of Torkkola and Amari, Cichocki and Yang, to give Natural Gradient information maximisation rules for recurrent (IIR) networks, blindly adjusting delays, separating and deconvolving mixed signals. While they work well on simulated data, these rules fail in real rooms which usually involve nonminimum phase transfer functions, notinvertible using stable IIR filters. An approach that sidesteps this problem is to perform infomax on a feedforward architecture in the frequency domain (Lambert 1996). We demonstrate realroom separation of two natural signals using this approach. 1 The problem. In the linear blind signal processing problem ([3, 2] and references therein), N signals, s(t) = [s 1 (t) : : : s N (t)] T , are transmitted through a medium so that an array of N sensors picks up a set of signals x(t) = [x 1 (t) : : : xN (t)] T , each of which has bee...
Ensemble learning for independent component analysis
 in Advances in Independent Component Analysis
, 2000
"... i Abstract This thesis is concerned with the problem of Blind Source Separation. Specifically we considerthe Independent Component Analysis (ICA) model in which a set of observations are modelled by xt = Ast: (1) where A is an unknown mixing matrix and st is a vector of hidden source components atti ..."
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Cited by 49 (2 self)
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i Abstract This thesis is concerned with the problem of Blind Source Separation. Specifically we considerthe Independent Component Analysis (ICA) model in which a set of observations are modelled by xt = Ast: (1) where A is an unknown mixing matrix and st is a vector of hidden source components attime t. The ICA problem is to find the sources given only a set of observations. In chapter 1, the blind source separation problem is introduced. In chapter 2 the methodof Ensemble Learning is explained. Chapter 3 applies Ensemble Learning to the ICA model and chapter 4 assesses the use of Ensemble Learning for model selection.Chapters 57 apply the Ensemble Learning ICA algorithm to data sets from physics (a medical imaging data set consisting of images of a tooth), biology (data sets from cDNAmicroarrays) and astrophysics (Planck image separation and galaxy spectra separation).
Evaluation Of Blind Signal Separation Methods
, 1999
"... Recently, many new Blind Signal Separation (BSS) algorithms have been introduced. Authors evaluate the performance of their algorithms in various ways. Among these are speech recognition rates, plots of separated signals, plots of cascaded mixing/unmixing impulse responses and signal to noise ratios ..."
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Cited by 42 (0 self)
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Recently, many new Blind Signal Separation (BSS) algorithms have been introduced. Authors evaluate the performance of their algorithms in various ways. Among these are speech recognition rates, plots of separated signals, plots of cascaded mixing/unmixing impulse responses and signal to noise ratios. Clearly, not all of these methods give a good reflection of the performance of these algorithms. Moreover, since the evaluation is done using different measures and different data, results cannot be compared. As a solution we provide a unified methodology of evaluating BSS algorithms along with providing data online such that researches can compare their results. We will focus on acoustical applications, but many of the remarks apply to other BSS application areas as well. 1. INTRODUCTION Blind Signal Separation (BSS) is the process that aims at separating a number of source signals from observed mixtures of those sources [1, 2, 3, 4, 5]. For example, in an acoustical application, these m...
Blind Source Separation Of Nonlinear Mixing Models
 In Neural networks for Signal Processing VII
, 1997
"... We present a new set of learning rules for the nonlinear blind source separation problem based on the information maximization criterion. The mixing model is divided into a linear mixing part and a nonlinear transfer channel. The proposed model focuses on a parametric sigmoidal nonlinearity and high ..."
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Cited by 30 (3 self)
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We present a new set of learning rules for the nonlinear blind source separation problem based on the information maximization criterion. The mixing model is divided into a linear mixing part and a nonlinear transfer channel. The proposed model focuses on a parametric sigmoidal nonlinearity and higher order polynomials. Our simulation results verify the convergence of the proposed algorithms. 1 INTRODUCTION In blind source separation or independent component analysis (ICA) the problem is how to recover independent sources given the sensor outputs in which the sources have been mixed in an unknown channel. The problem has become increasingly important in the signal processing area due to their prospective application in speech recognition, telecommunications and medical signal processing. The linear blind source separation problem has been studied by researchers in the field of neural networks [1, 2, 5, 9] and statistical signal processing [4, 6]. Potential application in automatic spe...
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.
Combining timedelayed decorrelation and ICA: Towards solving the cocktail party problem
 In Proc. ICASSP98
, 1998
"... We present methods to separate blindly mixed signals recorded in a room. The learning algorithm is based on the information maximization in a single layer neural network. We focus on the implementation of the learning algorithm and on issues that arise when separating speakers in room recordings. We ..."
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Cited by 21 (4 self)
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We present methods to separate blindly mixed signals recorded in a room. The learning algorithm is based on the information maximization in a single layer neural network. We focus on the implementation of the learning algorithm and on issues that arise when separating speakers in room recordings. We used an infomax approach in a feedforward neural network implemented in the frequency domain using the polynomial filter matrix algebra technique. Fast convergence speed was achieved by using a timedelayed decorrelation method as a preprocessing step. Under minimumphasemixing conditions this preprocessing step was sufficient for the separation of signals. These methods successfully separated a recorded voice with music in the background(cocktail party problem). Finally, we discuss problems that arise in real world recordings and their potential solutions. 1.
Independent component analysis based on nonparametric density estimation
 IEEE Trans. Neural Netw
, 2004
"... Abstract—In this paper, we introduce a novel independent component analysis (ICA) algorithm, which is truly blind to the particular underlying distribution of the mixed signals. Using a nonparametric kernel density estimation technique, the algorithm performs simultaneously the estimation of the unk ..."
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Cited by 21 (0 self)
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Abstract—In this paper, we introduce a novel independent component analysis (ICA) algorithm, which is truly blind to the particular underlying distribution of the mixed signals. Using a nonparametric kernel density estimation technique, the algorithm performs simultaneously the estimation of the unknown probability density functions of the source signals and the estimation of the unmixing matrix. Following the proposed approach, the blind signal separation framework can be posed as a nonlinear optimization problem, where a closed form expression of the cost function is available, and only the elements of the unmixing matrix appear as unknowns. We conducted a series of Monte Carlo simulations, involving linear mixtures of various source signals with different statistical characteristics and sample sizes. The new algorithm not only consistently outperformed all stateoftheart ICA methods, but also demonstrated the following properties: 1) Only a flexible model, capable of learning the source statistics, can consistently achieve an accurate separation of all the mixed signals. 2) Adopting a suitably designed optimization framework, it is possible to derive a flexible ICA algorithm that matches the stability and convergence properties of conventional algorithms. 3) A nonparametric approach does not necessarily require large sample sizes in order to outperform methods with fixed or partially adaptive contrast functions. Index Terms—Independent component analysis (ICA), kernel density estimation, nonlinear optimization, nonparametric methods. I.
A.: Blind signal deconvolution by spatiotemporal decorrelation and demixing. Neural Networks for Signal Processing 7
, 1997
"... In this paper we present a simple efficient local unsupervised learning algorithm for online adaptive multichannel blind deconvolution and separation of i.i.d. sources. Under mild conditions, there exits a stable inverse system so that the source signals can be exactly recovered from their convolut ..."
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Cited by 11 (4 self)
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In this paper we present a simple efficient local unsupervised learning algorithm for online adaptive multichannel blind deconvolution and separation of i.i.d. sources. Under mild conditions, there exits a stable inverse system so that the source signals can be exactly recovered from their convolutive mixtures. Based on the existence of the inverse filter, we construct a twostage neural network which consists of blind equalization and source separation. In blind equalization stage, we employ antiHebbian learning in temporal domain for decorrelation. For blind separation, we can apply any existing algorithms. Extensive computer simulations confirm the validity and high performance of our proposed learning algorithm. 1