We present an investigation into signal processing models appropriate for audio, and especially high quality musical signals, by means of Bayesian atomic decompositions. At present, many models rely on short-term stationarity of the audio, or highly limiting forms of non-stationarity. Moreover, they are well-suited only to low-level inference tasks. We seek to formulate a new generation of audio models that will address the main limitations of the existing ones and permit high-level inference. As we show, such models result from the marriage of an overcomplete dictionary of time-frequency atoms with structured hierarchical prior probability distributions developed specifically for audio signals, in order to model coefficient correlation in time and frequency.

Authors: Wolfe, Patrick J.; Godsill, Simon J.; Ng, Wee Jing; Doerfler, Monika
Affiliations: Department of Engineering, University of Cambridge,Cambridge, UK ; Institute for Mathematics, University of Vienna, Strudlhofgasse, Vienna, Austria(See document for exact affiliation information.)
AES Convention: 112 (April 2002) Paper Number: 5624
Publication Date: April 1, 2002 Import into BibTeX
Subject: Recording, Restoration, and Processing
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=11317

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD aes19)   /pp2002/pp0205/000238.pdf

Start a discussion about this paper!