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Dictionary learning of convolved signals

Barchiesi, D and Plumbley, MD (2011) Dictionary learning of convolved signals In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2011-05-22 - 2011-05-27, Prague Congress Ctr, Prague, CZECH REPUBLIC.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Barchiesi, DUNSPECIFIEDUNSPECIFIED
Plumbley, MDm.plumbley@surrey.ac.ukUNSPECIFIED
Date : 1 January 2011
Identification Number : 10.1109/ICASSP.2011.5947682
Copyright Disclaimer : © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Contributors :
ContributionNameEmailORCID
UNSPECIFIEDIEEE, UNSPECIFIEDUNSPECIFIED
Uncontrolled Keywords : Science & Technology, Technology, Acoustics, Engineering, Electrical & Electronic, Imaging Science & Photographic Technology, Engineering, Dictionary Learning, Sparse Representation, Convolution, K-SVD, SPARSE REPRESENTATION
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:19
Last Modified : 30 Nov 2017 09:18
URI: http://epubs.surrey.ac.uk/id/eprint/838770

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