Dictionary learning of convolved signals

Abstract : Assuming that a set of source signals is sparsely representable in a given dictionary, we show how their sparse recovery fails whenever we can only measure a convolved observation of them. Starting from this motivation, we develop a block coordinate descent method which aims to learn a convolved dictionary and provide a sparse representation of the observed signals with small residual norm. We compare the proposed approach to the K-SVD dictionary learning algorithm and show through numerical experiment on synthetic signals that, provided some conditions on the problem data, our technique converges in a fixed number of iterations to a sparse representation with smaller residual norm.
Type de document :
Communication dans un congrès
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, May 2011, Praha, Czech Republic. pp.5812 -5815, 2011, 〈10.1109/ICASSP.2011.5947682〉
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https://hal.inria.fr/hal-00705998
Contributeur : Jules Espiau de Lamaestre <>
Soumis le : vendredi 8 juin 2012 - 16:15:26
Dernière modification le : lundi 13 octobre 2014 - 15:43:25

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Daniele Barchiesi, Mark Plumbley. Dictionary learning of convolved signals. Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, May 2011, Praha, Czech Republic. pp.5812 -5815, 2011, 〈10.1109/ICASSP.2011.5947682〉. 〈hal-00705998〉

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