Quasi-Non-Sparse Component Analysis methods and their applications
Speaker: Matthieu Puigt
Date: 21 June 2010 Time: 11:00-13:00
Location: "Alkiviades C. Payatakes" Seminar Room, FORTH, Heraklion, Crete
Host: Athanasios Mouchtaris


Blind source separation (BSS) consists in estimating a set of unknown sources from a set of observations resulting from mixtures of these sources through unknown propagation channels. For that purpose, methods usually assume the sources to be statistically independent, non-negative and/or sparse. A signal is said to be sparse in a representation domain if most of its atoms are zero or close to zero. Most of sparsity-based BSS methods require the disjointness of the sources in the representation domain: in each atom, they assume that one and only one source is non-zero. On the contrary, a few approaches, including those I developed, require highly relaxed sparsity assumptions, hence their name of Quasi-Non-Sparse Component Analysis. In this presentation, I will briefly introduce such methods for linear instantaneous, anechoic, and convolutive mixtures and I will illustrate their performance with audio and astrophysical applications.


Matthieu Puigt received his PhD from the University of Toulouse (Laboratoire d'Astrophysique de Toulouse-Tarbes) in 2007. From 2007 to 2009 he was a post-doc fellow at the same laboratory. Since September 2009, he holds an assistant professor position at the University for Information Science and Technology "St Paul the Apostle", in Ohrid, FYROM. Matthieu Puigt's current research interests include signal processing, time-frequency and wavelet analysis, unsupervised classification, and especially Blind Source Separation methods and their applications to Acoustics and Astrophysics.

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