A Perceptually Reweighted Mixed-Norm Method for Sparse Approximation of Audio Signals
Publikation: Forskning - peer review › Konferenceartikel i tidsskrift
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 l1-norm based measure of sparsity; and 2) a reweighted l2-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.
|Tidsskrift||Asilomar Conference on Signals, Systems and Computers. Conference Record|
|Seminar||Asilomar Conference on Signals, Systems and computers, Nov. 6-9, 2011|
|Periode||06/11/11 → 09/11/11|
Ingen data tilgængelig