Selective background adaptation based abnormal acoustic event recognition for audio surveillance

Woohyun Choi, Jinsang Rho, David K. Han, Hanseok Ko

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

18 Scopus citations

Abstract

In this paper, a method for abnormal acoustic event recognition in an audio surveillance system is presented. We propose a recognition scheme based on a hierarchical structure using a feature combination of Mel-Frequency Cepstral Coefficient (MFCC), timbre, and spectral statistics. A selective background adaptation is proposed for robust abnormal acoustic event recognition in real-world situations. For training, we use a database containing 9 abnormal events (scream, glass breaking, and etc.) and 6 background noise types collected under various surveillance situations. Gaussian Mixture Model (GMM) is considered for classifying the representative abnormal acoustic events and for selecting the background noise for adaptation. Effectiveness of the proposed method is demonstrated via representative experimental results.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012
Pages118-123
Number of pages6
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012 - Beijing, China
Duration: Sep 18 2012Sep 21 2012

Publication series

NameProceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012

Conference

Conference2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012
Country/TerritoryChina
CityBeijing
Period9/18/129/21/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Keywords

  • Abnormal acoustic event recognition
  • Audio surveillance
  • Background adaptation

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