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Performance of Basic Spectral Descriptors and MRMR Algorithm to the Detection of Acoustic Events

  • Eva Vozarikova
  • Martin Lojka
  • Jozef Juhar
  • Anton Cizmar
Part of the Communications in Computer and Information Science book series (CCIS, volume 287)

Abstract

This paper is focused on the detection of abnormal situations via sound information. As a main feature extraction algorithm, basic spectral low - level descriptors defined in MPEG-7 standard were used. Various settings for spectral descriptors such as Audio Spectrum Envelope, Audio Spectrum Flatness, Audio Spectrum Centroid and Audio Spectrum Spread were used and many experiments were done for finding the limits of using them for the purpose of acoustic event detection in urban environment. For improving the recognition rate we also applied the feature selection algorithm called Minimum Redundancy Maximum Relevance. The proposed framework of recognizing potentially dangerous acoustic events such as breaking glass and gun shots, based on the extraction of basic spectral descriptors through well known Hidden Markov Models based classification is presented here.

Keywords

MPEG -7 feature selection MRMR acoustic events 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Eva Vozarikova
    • 1
  • Martin Lojka
    • 1
  • Jozef Juhar
    • 1
  • Anton Cizmar
    • 1
  1. 1.Dept. of Electronics and Multimedia Communications, FEI TU KosiceTechnical University of KosiceKosiceSlovak Republic

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