Self-adaptive neural networks for blind separation of sources

Cichocki, Andrzej; Амари, Шун-ичи; Adachi, Masaharu; Kasprzak, Włodzimierz

doi:10.1109/iscas.1996.540376

Cited by 35 publications

(20 citation statements)

References 7 publications

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“…Furthermore, their mixing and transmission processes are not well known in advance. In these kinds of situations, blind source separation (BSS) technologies using statistical properties of signal sources have become very important [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation

Horita¹,

Nakayama²,

Hirano³

2007

IEICE Transactions on Fundamentals of Electronics, Communicatio

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Akihide HORITA †a) , Student Member, Kenji NAKAYAMA †b) , and Akihiro HIRANO †c) , Members SUMMARY FeedForward (FF-) Blind Source Separation (BSS) systems have some degree of freedom in the solution space. Therefore, signal distortion is likely to occur. First, a criterion for the signal distortion is discussed. Properties of conventional methods proposed to suppress the signal distortion are analyzed. Next, a general condition for complete separation and distortion-free is derived for multi-channel FF-BSS systems. This condition is incorporated in learning algorithms as a distortion-free constraint. Computer simulations using speech signals and stationary colored signals are performed for the conventional methods and for the new learning algorithms employing the proposed distortion-free constraint. The proposed method can well suppress signal distortion, while maintaining a high source separation performance. key words: blind source separation, signal distortion, convergence, learning algorithm, convolutive mixture

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Section: Introductionmentioning

confidence: 99%

A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation

Horita¹,

Nakayama²,

Hirano³

2007

IEICE Transactions on Fundamentals of Electronics, Communicatio

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“…Furthermore, their mixing and transmission processes are not well known in advance. In these kind of situations, blind source separation (BSS) technology using statistical properties of signal sources have become very important [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

A Distortion Free Learning Algorithm for Feedforward BSS and ITS Comparative Study with Feedback BSS

Horita

Nakayama

Hirano

et al. 2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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“…In the latter portion of this paper, we consider just such an extension for decorrelation and source separation. An example of a somewhatdi erent philosophy can be found in 29], in which the parameter update terms are used directly within a nonlinear ltering structure. While heuristic, such an approach is more general than other cost-function-based approaches, as certain parameter adaptation rules for unsupervised training are not derived from the explicit minimization of a cost function.…”

Section: Adaptive Step Size Methodsmentioning

confidence: 99%

Adaptive step size techniques for decorrelation and blind source separation

Douglas

Cichocki²

Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284)

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Careful selection of step size parameters is often necessary to obtain good performance from gradient-based adaptive algorithms for decorrelation and source separation tasks. In this paper, we p r o vide an overview of methods for the on-line calculation of step size parameters for these systems. A particular emphasis is placed on gradient adaptive s t e p sizes for a class of natural gradient algorithms for decorrelation and blind source separation. Simulations verifying their useful behaviors are provided. INTRODUCTIONBlind source separation (BSS) is the task of separating multiple statistically-independent signals from observed linear mixtures. Useful for many signal processing tasks, BSS has received much recent research a t t e n tion, and numerous useful algorithms have been developed 1]{ 4]. In BSS, one measures a vector sequence x(k) = x1(k) xn(k)] T that is assumed to t the modelwhere s(k) = s1(k) sm(k)] T , m n, contains m independent source signals and H is an (n m) mixing matrix.In adaptive solutions to the BSS task, an output signal vec-where W(k) i s a n ( m n) matrix of adaptive parameters. Then, the goal is to adjust W(k) such that the combined system matrix C(k) = W(k)H evolves aswhere P and D are permutation and nonsingular diagonal scaling matrices, respectively, s u c h that each si(k) i n s(k) appears in y(k).One particularly-useful iterative technique for BSS is the natural gradient algorithm given byfm(ym(k))] T is a vector of nonlinearly-modi ed output signals. This algorithm attempts to minimize the entropy-based cost function EfJ (W(k))g whereand fi(y) = ;@ log pi(y)=@y. Numerous useful properties concerning the behavior of (4) and the natural gradient h a ve been given in the literature 3]{ 6]. Perhaps the most important property possessed by (4) from the standpoint o f performance is its uniform convergence behavior for arbitrary matrices H, a property k n o wn as equivariance 4].A related task to BSS is adaptive decorrelation (AD) or prewhitening, in which x(k) has an autocorrelation Rxx = Efx(k)x T (k)g:The goal of AD is to determine W(k) in (2) such thatRyy(k) = I:As many adaptive systems behave better when driven by decorrelated signals, AD is a useful preprocessing step in adaptive ltering, array processing, blind deconvolution/equalization, and multilayer perceptron training.It can be shown that choosing f(y(k)) = y(k) in (4) yields an equivariant AD algorithm. Alternatively, the computationally-simple update W(k + 1 ) = W(k) + (k) I ; y(k)y T (k)] (8) with W(0) chosen as a symmetric matrix can be used. Performance analyses of both (4) and (8) for AD indicate that they work as desired, and the analyses provide useful information for choosing (k) to obtain stable, robust behavior from these schemes 7].A critical challenge in the above tasks is the choice of step size (k) to achieve fast initial adaptation, a low steadystate error, and in the case of nonstationary or time-varying signal models, good tracking performance. In this paper, we develop gradient-based methods for...

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Self-adaptive neural networks for blind separation of sources

Cited by 35 publications

References 7 publications

A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation

A Distortion-Free Learning Algorithm for Feedforward Multi-Channel Blind Source Separation

A Distortion Free Learning Algorithm for Feedforward BSS and ITS Comparative Study with Feedback BSS

Adaptive step size techniques for decorrelation and blind source separation

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