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
CountryChina
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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