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Algorithmes temporels rapides à point fixe pour la séparation aveugle de mélanges convolutifs et/ou sous-déterminés.

Abstract : The first part of this thesis deals with (over-)determined convolutive mixtures. We extend the FastICA algorithm to convolutive mixtures by developing a fixed-point algorithm operating in the time domain. We introduce a non-causal spatio-temporal whitening process, which, by initializing the extraction parameters in a particular way, allows us to use fixed-point optimization iterations. The estimation of the contributions in the observations is realized by means of a quadratic criterion optimized by a non-causal Wiener process.
In the second part concerning instantaneous and convolutive under-determined mixtures, we extend the FastICA algorithm by using the differential separation concept. We propose differential observation whitening procedures which allow us to use fixed-point iterations to separate between them some sources of interest. In the convolutive case, the contributions are estimated thanks to a non-causal differential Wiener process.
The third part deals with instantaneous mixtures of sources containing a high number of samples. We first point out a limitation of the FastICA algorithm which, at each optimization iteration, computes some statistics on all available samples. As the kurtosis criterion is polynomial relatively to the extraction coefficients, we propose a procedure to identify this polynomial in order to perform the optimization in a more efficient computation space. This principle is then extended to underdetermined instantaneous mixtures
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Contributor : Williams Exbrayat <>
Submitted on : Monday, March 31, 2008 - 3:41:29 PM
Last modification on : Thursday, October 15, 2020 - 4:06:55 AM
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  • HAL Id : tel-00268239, version 1



Johan Thomas. Algorithmes temporels rapides à point fixe pour la séparation aveugle de mélanges convolutifs et/ou sous-déterminés.. Astrophysique [astro-ph]. Université Paul Sabatier - Toulouse III, 2007. Français. ⟨tel-00268239⟩



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