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Conference Papers Year : 2011

Dictionary learning of convolved signals

Daniele Barchiesi
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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.

Dates and versions

hal-00705998 , version 1 (08-06-2012)



Daniele Barchiesi, Mark D. 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, ⟨10.1109/ICASSP.2011.5947682⟩. ⟨hal-00705998⟩
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