Results 1  10
of
15
An informationmaximization approach to blind separation and blind deconvolution
 NEURAL COMPUTATION
, 1995
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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...
Monaural separation of independent acoustical components
 In IEEE International Symposium on Circuits and Systems. ISCAS 1999
, 1999
"... The problem of blindly separating signal mixtures with fewer mixture components than independent signal sources is mathematically illdefined, and requires suitable prior information on the nature of the sources. Recently, it has been shown that sparse methods for function approximation using a Lapl ..."
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Cited by 15 (1 self)
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The problem of blindly separating signal mixtures with fewer mixture components than independent signal sources is mathematically illdefined, and requires suitable prior information on the nature of the sources. Recently, it has been shown that sparse methods for function approximation using a Laplacian prior can be effective, but the method fails to separate a single mixture without further prior information. Other techniques track harmonics, but assume separability in the timefrequency domain. We show that a measure of temporal and spectral coherence provides an effective cue for separating independent acoustical or sonar sources, in the absence of spatial cues in the monaural case. The technique is shown to successfully separate single mixtures of sources with significant spectral overlap. 1.
Blind Separation Of Linear Convolutive Mixtures Using Orthogonal Filter Banks
 Proc. IEEE Int. Symp. Circuits and Systems (ISCAS’98), Monterey CA
, 2001
"... We propose an algorithm and architecture for realtime blind source separation of linear convolutive mixtures using orthogonal filter banks. The adaptive algorithm derives from stochastic gradient descent optimization of a performance metric that quantifies independence not only across the reconstru ..."
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Cited by 10 (6 self)
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We propose an algorithm and architecture for realtime blind source separation of linear convolutive mixtures using orthogonal filter banks. The adaptive algorithm derives from stochastic gradient descent optimization of a performance metric that quantifies independence not only across the reconstructed sources, but also across time within each source. The special case of a Laguerre section offers a compact representation with a small number of filter taps even under severe reverberant conditions, facilitating realtime implementation in a modular and scalable parallel architecture. Simulations of the proposed architecture and update rule validate the approach.
Fourth Order Criteria for Blind Sources Separation
 IEEE transactions on signal processing. vol
, 1995
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Gradient flow adaptive beamforming and signal separation in a miniature microphone array
 Proc. IEEE Int. Conf. Acoustics Speech and Signal Processing (ICASSP’2002), (Orlando FL
, 2002
"... Gradient flow converts the problem of separating unknown delayed mixtures of sources, from traveling waves impinging on an array of sensors, into a simpler problem of separating unknown instantaneous mixtures of the timedifferentiated sources, obtained by acquiring or computing spatial and temporal ..."
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Cited by 3 (1 self)
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Gradient flow converts the problem of separating unknown delayed mixtures of sources, from traveling waves impinging on an array of sensors, into a simpler problem of separating unknown instantaneous mixtures of the timedifferentiated sources, obtained by acquiring or computing spatial and temporal derivatives on the array. The linear coefficients in the instantaneous mixture directly represent the delays, which in turn determine the direction angles of the sources. This formulation is attractive, since it allows to separate and localize waves of broadband signals using standard tools of independent component analysis (ICA), yielding the sources along with their direction angles. The technique is suited for arrays of small aperture, with dimensions shorter than the coherence length of the waves. We present gradient flow experiments on an array of four hearing aid microphones placed within a 5 mm radius, yielding 20 dB separation of joint speech in outdoors acoustic environments, and 10 dB separation indoors under mild reverberant conditions. These results suggest applications of gradient flow miniature microphone arrays to intelligent hearing aids with adaptive suppression of interfering signals and nonstationary noise. 1.
Blind broadband source localization and separation in miniature sensor arrays
 in Proc. IEEE Int. Symp. Circuits and Systems (ISCAS’2001
"... We study the ability of a sensor array to blindly separate and localize broadband traveling waves impinging on the array, with additive sensor noise. We consider arrays smaller than the shortest wavelength in the sources, such as MEMS acoustic arrays or VLSI arrays of RF receivers. A series expansio ..."
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Cited by 2 (2 self)
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We study the ability of a sensor array to blindly separate and localize broadband traveling waves impinging on the array, with additive sensor noise. We consider arrays smaller than the shortest wavelength in the sources, such as MEMS acoustic arrays or VLSI arrays of RF receivers. A series expansion about the center of the array of the timedelayed signals emanating from the sources reduces the problem of separating and localizing the delayed sources to that of separating instantaneous signal mixtures using conventional tools of Independent Component Analysis. The covariance of the noise in the estimated sources is expressed in terms of the covariance of the sensor noise and the angular direction of the sources. Physical simulations demonstrate separation and localization of three noncoplanar speech sources using a planar array of four sensors within a 1 mm radius. 1.
Implementation of Infomax ICA Algorithm with Analog CMOS Circuits
, 2001
"... Independent Component Analysis algorithm based on infomax theory with natural gradient was implemented with a fullyanalog CMOS chip. Although one chip consists of 4 inputs and 4 outputs, the chip incorporates fullymodular architecture for multichip applications. The fabricated chip demonstrated i ..."
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Cited by 2 (1 self)
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Independent Component Analysis algorithm based on infomax theory with natural gradient was implemented with a fullyanalog CMOS chip. Although one chip consists of 4 inputs and 4 outputs, the chip incorporates fullymodular architecture for multichip applications. The fabricated chip demonstrated improved SNRs for unknown speech mixtures.
A Unifying Informationtheoretic Framework for Independent Component Analysis
, 1998
"... 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. ..."
Abstract
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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). L...