Paper Info
Title
A perceptually reweighted mixed-norm method for sparse approximation of audio signals
Abstract
In this paper, we consider the problem of finding sparse representations of audio signals for coding purposes. In doing so, it is of utmost importance that when only a subset of the present components of an audio signal are extracted, it is the perceptually most important ones. To this end, we propose a new iterative algorithm based on two principles: 1) a reweighted 1-norm based measure of sparsity; and 2) a reweighted 2-norm based measure of perceptual distortion. Using these measures, the considered problem is posed as a constrained convex optimization problem that can be solved optimally using standard software. A prominent feature of the new method is that it solves a problem that is closely related to the objective of coding, namely rate-distortion optimization. In computer simulations, we demonstrate the properties of the algorithm and its application to real audio signals.
Year
DOI
Venue
2011
10.1109/ACSSC.2011.6190067
Signals, Systems and Computers
Keywords
Field
DocType
audio coding,convex programming,iterative methods,rate distortion theory,signal representation,1-norm based measure of sparsity,audio coding,audio signals,convex optimization,iterative algorithm,perceptual distortion measure,perceptually reweighted mixed-norm method,rate-distortion optimization,sparse approximation,sparse representation,Audio coding,audio modeling,perceptual distortion measures,sparse approximations
Audio signal,Mathematical optimization,Pattern recognition,Computer science,Iterative method,Sparse approximation,Perceptual Distortion,Coding (social sciences),Software,Artificial intelligence,Convex optimization,Rate–distortion theory
Conference
ISSN
ISBN
Citations 
1058-6393
978-1-4673-0321-7
2
PageRank 
References 
Authors
0.50
6
2
Name
Order
Citations
PageRank
Mads Grísbøll Christensen176176.48
Bob L. Sturm224129.88