Detection of abnormal sound using multi-stage GMM for surveillance microphone

Akinori Ito, Akihito Aiba, Masashi Ito, Shozo Makino

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

11 Citations (Scopus)

Abstract

We developed a system that detects abnormal sound from sound signal observed by a surveillance microphone. Our system learns the "normal sound" from observation of the microphone, and then detects sounds never observed before as "abnormal sounds." To this end, we developed a technique that uses multiple GMMs for modeling different levels of sound events efficiently. We also consider how to determine thresholds of GMM switching and event detection. As a result, we obtained almost same detection performance using the percentile method to the manually optimized GMMs. Besides, we exploited the segment-based feature, which gave the best result among all methods.

Original languageEnglish
Title of host publication5th International Conference on Information Assurance and Security, IAS 2009
Pages733-736
Number of pages4
DOIs
Publication statusPublished - 2009 Dec 1
Event5th International Conference on Information Assurance and Security, IAS 2009 - Xian, China
Duration: 2009 Aug 182009 Sep 20

Publication series

Name5th International Conference on Information Assurance and Security, IAS 2009
Volume1

Other

Other5th International Conference on Information Assurance and Security, IAS 2009
Country/TerritoryChina
CityXian
Period09/8/1809/9/20

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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