Séparation aveugle de source : de l'instantané au convolutif

Abstract : Blind source separation (BSS) consists of estimating the source signals only from the observed mixtures. The problem can be divided into two categories according to the mixing model: instantaneous mixtures, where delay and reverberation (multi-path effect) are not taken into account, and convolutive mixtures which are more general but more complicated. Moreover, the additive noise at the sensor level and the underdetermined setting, where there are fewer sensors than the sources, make the problem even more difficult.In this thesis, we first studied the link between two existing methods for instantaneous mixtures: independent component analysis (ICA) and sparse component analysis (SCA). We then proposed a new formulation that works in both determined and underdetermined cases, with and without noise. Numerical evaluations show the advantage of the proposed approaches.Secondly, the proposed formulation is generalized for convolutive mixtures with speech signals. By integrating a new approximation model, the proposed algorithms work better than existing methods, especially in noisy and/or high reverberation scenarios.Then, we take into account the technique of morphological decomposition and the use of structured sparsity which leads to algorithms that can better exploit the structures of audio signals. Such approaches are tested for underdetermined convolutive mixtures in a non-blind scenario.At last, being benefited from the NMF model, we combined the low-rank and sparsity assumption and proposed new approaches for under-determined convolutive mixtures. The experiments illustrate the good performance of the proposed algorithms for music signals, especially in strong reverberation scenarios.
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Submitted on : Friday, October 13, 2017 - 2:47:09 PM
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  • HAL Id : tel-01760974, version 2


Fangchen Feng. Séparation aveugle de source : de l'instantané au convolutif. Traitement du signal et de l'image [eess.SP]. Université Paris-Saclay, 2017. Français. ⟨NNT : 2017SACLS232⟩. ⟨tel-01760974v2⟩



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