Blind separation of wide-band sources in the frequency domain

Capdevielle, Véronique; Servière, Christine; Lacoume, Jean-Louis

doi:10.1109/icassp.1995.478484

Cited by 57 publications

(46 citation statements)

References 6 publications

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“…Sources (Parra) The main idea of the algorithm [7] proposed by L. Parra and C. Spence is similar to the previous ones [2] and [8]. This algorithm will be called later "Parra".…”

Section: Convolutive Blind Separation Of Non-stationarymentioning

confidence: 95%

Blind Separation of Underwater Acoustic Signals

Mansour

Benchekroun

Gervaise

2006

Independent Component Analysis and Blind Signal Separation

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Abstract-Acoustic tomography is an issue of great importance in many applications. Few algorithms have been dedicated to the passive acoustic tomography of a single input single output (SISO) channel. As matter of fact, most of those algorithms can not be applied in a real situation i.e. for a Multi-Input MultiOutput (MIMO) channel. In this paper, we discussed at first the features of the acoustic channel and signals then we proposed an architecture to separate acoustic signals issued from an acoustic realistic channel. The proposed structures used algorithms based on independent component analysis (ICA). We should mention here that many algorithms have implemented and tested but only two algorithms give good results. The latter algorithms minimize two different second order statistic criteria in the frequency domain. Finally, some simulations have been presented and discussed.

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“…Sources (Parra) The main idea of the algorithm [7] proposed by L. Parra and C. Spence is similar to the previous ones [2] and [8]. This algorithm will be called later "Parra".…”

Section: Convolutive Blind Separation Of Non-stationarymentioning

confidence: 95%

Blind Separation of Underwater Acoustic Signals

Mansour

Benchekroun

Gervaise

2006

Independent Component Analysis and Blind Signal Separation

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“…For a review of this approach, the reader is referred to [14]. Capdevielle et al [3] have proposed a method to avoid the permutation indeterminacy by recovering the continuity of the frequency spectra. Due to the independence of the sources, the cross-correlation between the frequency-domain outputs corresponding to different sources is zero and, therefore, the frequency-domain outputs corresponding to the same source can be determined by maximizing the cross-correlation between them.…”

Section: Introductionmentioning

confidence: 99%

An MSE-Based Method to Avoid Permutation/Gain Indeterminacy in Frequency-Domain Blind Source Separation

Dapena

Iglesia

Escudero

2010

Circuits Syst Signal Process

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This paper presents a novel technique for separating convolutive mixtures of statistically independent non-Gaussian signals without resorting to an a priori knowledge of the sources or the mixing system. This problem is solved in the frequency domain by transforming the convolutive mixture into several instantaneous mixtures which are independently separated using blind source separation (BSS) algorithms. First, the instantaneous mixture at one frequency is solved using the joint approximate diagonalization of eigenmatrices (JADE) technique, and the other mixtures are then separated using the mean squared error (MSE) criterion. As a special case of this method, we consider the separation of non-Gaussian temporally white signals transmitted in blocks with zero padding between them.

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“…Other radio communication examples arise in the use of polarization multiplexing in microwave links because the orthogonality of the polarization cannot be maintained perfectly and there is interference among the separate transmissions. In wireless communication the problem arises of recovering communication signals (that are transmitted by unknown channels) from the received signals in the presence of both interuser and intersymbol interferences [19], [23], [61], [88], [106]. Other promising applications are in the area of noninvasive medical diagnosis and biomedical signal analysis, such as EEG, MEG, and ECG [78].…”

Section: Introductionmentioning

confidence: 99%