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Consistent dictionary learning for signal declipping

Rencker, Lucas, Bach, F, Wang, Wenwu and Plumbley, Mark D (2018) Consistent dictionary learning for signal declipping In: 14th International Conference on Latent Variable Analysis and Signal Separation, 2 - 6 July 2018, University of Surrey, Guildford, UK.

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Abstract

Clipping, or saturation, is a common nonlinear distortion in signal processing. Recently, declipping techniques have been proposed based on sparse decomposition of the clipped signals on a fixed dictionary, with additional constraints on the amplitude of the clipped samples. Here we propose a dictionary learning approach, where the dictionary is directly learned from the clipped measurements. We propose a soft-consistency metric that minimizes the distance to a convex feasibility set, and takes into account our knowledge about the clipping process. We then propose a gradient descent-based dictionary learning algorithm that minimizes the proposed metric, and is thus consistent with the clipping measurement. Experiments show that the proposed algorithm outperforms other dictionary learning algorithms applied to clipped signals. We also show that learning the dictionary directly from the clipped signals outperforms consistent sparse coding with a fixed dictionary.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Rencker, Lucaslucas.rencker@surrey.ac.uk
Bach, F
Wang, WenwuW.Wang@surrey.ac.uk
Plumbley, Mark Dm.plumbley@surrey.ac.uk
Date : 2018
Copyright Disclaimer : © 2018 Springer International Publishing AG, part of Springer Nature. The final authenticated version will be available online at http://www.springer.com/gp/computer-science/lncs
Related URLs :
Depositing User : Melanie Hughes
Date Deposited : 10 Apr 2018 08:31
Last Modified : 10 Apr 2018 13:18
URI: http://epubs.surrey.ac.uk/id/eprint/846156

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