Results 1  10
of
59
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 1492 (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
Convolutive Blind Separation of NonStationary
"... Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle the probl ..."
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Cited by 129 (3 self)
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Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle the problem by explicitly exploiting the nonstationarity of the acoustic sources. Changing crosscorrelations at multiple times give a sufficient set of constraints for the unknown channels. A least squares optimization allows us to estimate a forward model, identifying thus the multipath channel. In the same manner we can find an FIR backward model, which generates well separated model sources. Furthermore, for more than three channels we have sufficient conditions to estimate underlying additive sensor noise powers. We show good performance in real room environments and demonstrate the algorithm's utility for automatic speech recognition.
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...
Blind Separation of Disjoint Orthogonal Signals: Demixing N Sources from 2 Mixtures
 In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP
, 2000
"... We present a novel method for blind separation of any number of sources using only two mixtures. The method applies when sources are (W)disjoint orthogonal, that is, when the supports of the (windowed) Fourier transform of any two signals in the mixture are disjoint sets. We show that, for anechoi ..."
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Cited by 73 (7 self)
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We present a novel method for blind separation of any number of sources using only two mixtures. The method applies when sources are (W)disjoint orthogonal, that is, when the supports of the (windowed) Fourier transform of any two signals in the mixture are disjoint sets. We show that, for anechoic mixtures of attenuated and delayed sources, the method allows one to estimate the mixing parameters by clustering ratios of the timefrequency representations of the mixtures. The estimates of the mixing parameters are then used to partition the timefrequency representation of one mixture to recover the original sources. The technique is valid even in the case when the number of sources is larger than the number of mixtures. The general results are verified on both speech and wireless signals. Sample sound files can be found here: http://www.princeton.edu/~srickard/bss.html 1. INTRODUCTION Demixing noisy mixtures has been a goal of long standing in the field of blind source separation(...
Geometric source separation: Merging convolutive source separation with geometric beamforming
 IEEE Transactions on Speech and Audio Processing
, 2002
"... Abstract. Blind source separation of broad band signals in a multipath environment remains a di cult problem. Robustness has been limited due to frequency permutation ambiguities. Increasing the number of sensors allows improved performance but introduces degrees of freedom in the separating lters ..."
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Cited by 57 (3 self)
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Abstract. Blind source separation of broad band signals in a multipath environment remains a di cult problem. Robustness has been limited due to frequency permutation ambiguities. Increasing the number of sensors allows improved performance but introduces degrees of freedom in the separating lters that are not determined by separation criteria. We propose to further shape the lters and improve the robustness of blind separation by including geometric information such as sensor positions and localized source assumption. This allows us to combine blind source separation with adaptive and geometric beamforming leading to a number of novel algorithms collectively termed \geometric source separation". Performance comparisons on real room recordings for 2and3 simultaneous sources are presented.
A first application of independent component analysis to extracting structure from stock returns
 INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
, 1997
"... This paper discusses the application of a modern signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series int ..."
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Cited by 57 (1 self)
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This paper discusses the application of a modern signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). This can be viewed as a factorization of the portfolio since joint probabilities become simple products in the coordinate system of the ICs. We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent but large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. Independent component analysis is a potentially powerful method of analyzing and understanding driving mechanisms in financial markets. There are further
Fundamental Limitation Of Frequency Domain Blind Source Separation For Convolved Mixture Of Speech
 IEEE Trans. Speech Audio Process
, 2001
"... Despite several recent proposals to achieve Blind Source Separation (BSS) for realistic acoustic signals, the separation performance is still not enough. In particular, when the length of an impulse response is long, the performance is highly limited. In this paper, we consider the reason for the po ..."
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Cited by 55 (10 self)
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Despite several recent proposals to achieve Blind Source Separation (BSS) for realistic acoustic signals, the separation performance is still not enough. In particular, when the length of an impulse response is long, the performance is highly limited. In this paper, we consider the reason for the poor performance of BSS in a long reverberation environment. First, we show that it is useless to be constrained by the condition P << T, where T is the frame size of FFT and P is the length of a room impulse response. We also discuss the limitation of frequency domain BSS, by showing that the frequency domain BSS framework is equivalent to two sets of frequency domain adaptive beamformers.
A generalization of blind source separation algorithms for convolutive mixtures based on secondorder statistics
 IEEE TRANS. SPEECH AUDIO PROCESSING
, 2005
"... In this paper, we present a general broadband approach to blind source separation (BSS) for convolutive mixtures based on secondorder statistics. This avoids several known limitations of the conventional narrowband approximation, such as the internal permutation problem. In contrast to traditional ..."
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Cited by 44 (19 self)
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In this paper, we present a general broadband approach to blind source separation (BSS) for convolutive mixtures based on secondorder statistics. This avoids several known limitations of the conventional narrowband approximation, such as the internal permutation problem. In contrast to traditional narrowband approaches, the new framework simultaneously exploits the nonwhiteness property and nonstationarity property of the source signals. Using a novel matrix formulation, we rigorously derive the corresponding timedomain and frequencydomain broadband algorithms by generalizing a known costfunction which inherently allows joint optimization for several timelags of the correlations. Based on the broadband approach timedomain, constraints are obtained which provide a deeper understanding of the internal permutation problem in traditional narrowband frequencydomain BSS. For both the timedomain and the frequencydomain versions, we discuss links to wellknown, and also, to novel algorithms that constitute special cases. Moreover, using the socalled generalized coherence, links between the timedomain and the frequencydomain algorithms can be established, showing that our cost function leads to an update equation with an inherent normalization ensuring a robust adaptation behavior. The concept is applicable to offline, online, and blockonline algorithms by introducing a general weighting function allowing for tracking of timevarying real acoustic environments.
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 43 (3 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.