Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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)

A peer-reviewed article of this Preprint also exists.

Rajmic, P.; Záviška, P.; Veselý, V.; Mokrý, O. A New Generalized Projection and Its Application to Acceleration of Audio Declipping. Axioms 2019, 8, 105. Rajmic, P.; Záviška, P.; Veselý, V.; Mokrý, O. A New Generalized Projection and Its Application to Acceleration of Audio Declipping. Axioms 2019, 8, 105.

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.

Keywords

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

Subject

Computer Science and Mathematics, Computational Mathematics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.