Preprint Article Version 1 This version is not peer-reviewed

New Generalized Projection Speeds Up Audio Declipping

Version 1 : Received: 6 March 2019 / Approved: 7 March 2019 / Online: 7 March 2019 (12:11:19 CET)

How to cite: Rajmic, P.; Záviška, P.; Veselý, V.; Mokrý, O. New Generalized Projection Speeds Up Audio Declipping. Preprints 2019, 2019030093 (doi: 10.20944/preprints201903.0093.v1). Rajmic, P.; Záviška, P.; Veselý, V.; Mokrý, O. New Generalized Projection Speeds Up Audio Declipping. Preprints 2019, 2019030093 (doi: 10.20944/preprints201903.0093.v1).

Abstract

In theory and applications, it is often inevitable to work with projectors onto convex sets, where a linear transform is involved. In this article, a novel projector is presented, which generalizes previous results in that it admits a broader family of linear transforms, but on the other hand it is limited to box-type convex sets in the transformed domain. The new projector has an explicit formula and it can be interpreted within the framework of proximal optimization. The benefit of the new projector is demonstrated on an example from signal processing, where it was possible to speed up the convergence of a signal declipping algorithm by a factor of more than two.

Subject Areas

projection; optimization; generalization; box constraints; declipping; desaturation; proximal splitting; sparsity

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