Multivariate Iterative Hard Thresholding for sparse decomposition with flexible sparsity patterns

Rencker, Lucas, Wang, Wenwu and Plumbley, Mark (2017) Multivariate Iterative Hard Thresholding for sparse decomposition with flexible sparsity patterns In: European Signal Processing Conference (EUSIPCO) 2017, 28 Aug - 02 Sep 2017, Kos, Greece.

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Abstract

We address the problem of decomposing several consecutive sparse signals, such as audio time frames or image patches. A typical approach is to process each signal sequentially and independently, with an arbitrary sparsity level fixed for each signal. Here, we propose to process several frames simultaneously, allowing for more flexible sparsity patterns to be considered. We propose a multivariate sparse coding approach, where sparsity is enforced on average across several frames. We propose a Multivariate Iterative Hard Thresholding to solve this problem. The usefulness of the proposed approach is demonstrated on audio coding and denoising tasks. Experiments show that the proposed approach leads to better results when the signal contains both transients and tonal components.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Rencker, Lucas lucas.rencker@surrey.ac.uk Wang, Wenwu W.Wang@surrey.ac.uk Plumbley, Mark m.plumbley@surrey.ac.uk
Date :	26 October 2017
Funders :	Engineering and Physical Sciences Research Council (EPSRC)
DOI :	10.23919/EUSIPCO.2017.8081591
Copyright Disclaimer :	2017 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
Related URLs :	Organisation
Depositing User :	Clive Harris
Date Deposited :	29 Aug 2017 10:45
Last Modified :	11 Dec 2018 11:23
URI:	http://epubs.surrey.ac.uk/id/eprint/842062

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