A General Algebraic Algorithm for Blind Extraction of One Source in a MIMO Convolutive Mixture

Dubroca, Remi; Luigi, Christophe De; Castella, Marc; Moreau, Éric

doi:10.1109/tsp.2010.2042487

Cited by 13 publications

(10 citation statements)

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“…These criteria are known to provide good results. On the other hand, contrast functions referred to as "referencebased" have been recently proposed [11], [12]. They are particularly appealing because the corresponding maximization problem is quadratic with respect to the searched parameters.…”

Section: A Generalitiesmentioning

confidence: 99%

New Kurtosis Optimization Schemes for MISO Equalization

Castella

Moreau

2012

IEEE Trans. Signal Process.

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Abstract-This paper deals with efficient optimization of cumulant based contrast functions. Such a problem occurs in the blind source separation framework, where contrast functions are criteria to be maximized in order to retrieve the sources. More precisely, we focus on the extraction of one source signal and our method applies in deflation approaches, where the sources are extracted one by one.We propose new methods to maximize the kurtosis contrast function. These methods are intermediate between a gradient and an iterative "fixed-point" optimization of so-called reference contrasts. They rely on iterative updates of the parameters which monotonically increase the contrast function value: we point out the strong similarity with the EM (Expectation-Maximization) method and with recent generalizations referred to as MM (Minimization-Maximization). We also prove the global convergence of the algorithm to a stationary point. Simulations confirm the convergence of our methods to a separating solution. They also show experimentally that our methods have a much lower computational cost than former classical optimization methods. Finally, simulations suggest that the methods remain valid under weaker conditions than those required for proving convergence.

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Section: A Generalitiesmentioning

confidence: 99%

New Kurtosis Optimization Schemes for MISO Equalization

Castella

Moreau

2012

IEEE Trans. Signal Process.

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“…In this case, the sources undergo propagation time delay and reverberation which results in convolutive mixing. In the recent decades, various approaches have been proposed to deal with this case and they basically fall into two categories: time domain [8,9] and frequency domain [10][11][12] algorithms. Time domain methods are mainly inspired by the existing blind deconvolution methods and the solutions usually require intensive computations due to the relationship of filter coefficients with each other.…”

Section: Introductionmentioning

confidence: 99%

Decoupled Independent Vector Analysis Algorithm for Convolutive Blind Source Separation without Orthogonality Constraint on the Demixing Matrices

Wang

Zhang

2018

Mathematical Problems in Engineering

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In this paper, we consider the problem of convolutive blind source separation in frequency domain and introduce a solution to the problem in an independent vector analysis (IVA) framework. IVA utilizes both the statistical independence of different sources in each frequency bin and the statistical dependence of the same source in different frequency bins. However, most of previous works impose orthogonality constraint on the rows of each separation matrix which may undermine the separation performance. In this work, we propose a nonorthogonal IVA algorithm based on decoupled relative Newton method. This proposed algorithm updates the separation matrices row by row, and unlike deflation separation algorithm, there is no separation error accumulation arising. Simulation results are provided to show the superior convergence behavior and separation performance of the proposed algorithm.

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“…On the other hand, contrast functions referred to as "reference-based" have been recently proposed [19], [20]. The output variables in the traditional contrast functions are decoupled into several relatively independent ones, so it is the same with extraction vectors.…”

Section: Introductionmentioning

confidence: 99%