A causal Locally Competitive Algorithm for the sparse decomposition of audio signals

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 (US)
Title of host publication2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings
Pages265-270
Number of pages6
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Sedona, AZ, United States
Duration: Jan 4 2011Jan 7 2011

Publication series

Name2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings

Conference

Conference2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011
Country/TerritoryUnited States
CitySedona, AZ
Period1/4/111/7/11

Keywords

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

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Education

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