A Causal Locally Competitive Algorithm for the Sparse Decomposition of Audio Signals

Adam S. Charles, Abigail Anne Kressner, Christopher J. Rozell

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

While current inference methods can decompose audio signals, they require the entire signal upfront and are therefore ill-suited for real-time applications requiring causal processing. We propose a neurally-inspired, causal, sparse inference scheme based on the Locally Competitive Algorithm (LCA) over a temporal-spectral neighborhood. We demonstrate that this causal inference scheme can achieve lower sparsity levels and better signal fidelity than current filter and threshold approaches. Additionally, for some regimes, the sparsity level approaches those of Matching Pursuit while still maintaining signal integrity.
Original languageEnglish
Title of host publicationProceedings of 2011 IEEE Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop
PublisherIEEE
Publication date2011
Pages265-270
ISBN (Print)978-1-61284-226-4
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventDigital Signal Processing Workshop and IEEE Signal Processing Education Workshop - Sedona, AZ, United States
Duration: 4 Jan 20117 Jan 2011

Conference

ConferenceDigital Signal Processing Workshop and IEEE Signal Processing Education Workshop
CountryUnited States
CitySedona, AZ
Period04/01/201107/01/2011

Keywords

  • ENGINEERING,
  • GAMMACHIRP
  • Locally Competitive Algorithm (LCA)
  • causal sparse encoding
  • audio processing
  • convolutional model

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