Matching pursuit classification for time-varying acoustic emissions

Samuel P. Ebenezer, Antonia Papandreou-Suppappola, Seth B. Suppappola

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

10 Scopus citations


Time-varying techniques have been shown to be successful for classifying acoustic signals. In this paper, we propose a time-frequency based method for classifying time-varying, transient acoustic emissions using the matching pursuit decomposition (MPD) algorithm. Before classification, the MPD dictionary is formed using time and frequency shifted versions of selected learning signals. We show the superior classification performance of this method by applying it to real data obtained from an acoustic monitoring system. The main objective of an acoustic signal classifier in this system is to identify the acoustic emissions that may occur in concrete structures in distress. We study different strategies for binary and M-class classification, their dependence on the learning signals, and our classification results. We also discuss techniques to reduce the MPD processing time by pre-processing the test signals.

Original languageEnglish (US)
Title of host publicationConference Record of the Asilomar Conference on Signals, Systems and Computers
EditorsM.B. Matthews
Number of pages5
StatePublished - 2001
Event35th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 4 2001Nov 7 2001


Other35th Asilomar Conference on Signals, Systems and Computers
Country/TerritoryUnited States
CityPacific Grove, CA

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering


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