Audio signal representations for factorization in the sparse domain

Manuel Moussallam, Laurent Daudet, Gaël Richard

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a new class of audio representations is introduced, together with a corresponding fast decomposition algorithm. The main feature of these representations is that they are both sparse and approximately shift-invariant, which allows similarity search in a sparse domain. The common sparse support of detected similar patterns is then used to factorize their representations. The potential of this method for simultaneous structural analysis and compressing tasks is illustrated by preliminary experiments on simple musical data.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages513-516
Number of pages4
DOIs
Publication statusPublished - 18 Aug 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • Audio Signal Decomposition
  • Audio Similarity
  • Factorization
  • Matching Pursuit
  • Sparse Representation

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