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

Akinori Ito; Akihito Aiba; Masashi Ito; Shozo Makino

doi:10.1109/IAS.2009.160

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

Akinori Ito, Akihito Aiba, Masashi Ito, Shozo Makino

ENG - Communications Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	5th International Conference on Information Assurance and Security, IAS 2009
Pages	733-736
Number of pages	4
DOIs	https://doi.org/10.1109/IAS.2009.160
Publication status	Published - 2009 Dec 1
Event	5th International Conference on Information Assurance and Security, IAS 2009 - Xian, China Duration: 2009 Aug 18 → 2009 Sep 20

Publication series

Name	5th International Conference on Information Assurance and Security, IAS 2009
Volume	1

Other

Other	5th International Conference on Information Assurance and Security, IAS 2009
Country/Territory	China
City	Xian
Period	09/8/18 → 09/9/20

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Hardware and Architecture
Software

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10.1109/IAS.2009.160

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Detection of abnormal sound using multi-stage GMM for surveillance microphone. / Ito, Akinori; Aiba, Akihito; Ito, Masashi; Makino, Shozo.

5th International Conference on Information Assurance and Security, IAS 2009. 2009. p. 733-736 5283337 (5th International Conference on Information Assurance and Security, IAS 2009; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ito, A, Aiba, A, Ito, M & Makino, S 2009, Detection of abnormal sound using multi-stage GMM for surveillance microphone. in 5th International Conference on Information Assurance and Security, IAS 2009., 5283337, 5th International Conference on Information Assurance and Security, IAS 2009, vol. 1, pp. 733-736, 5th International Conference on Information Assurance and Security, IAS 2009, Xian, China, 09/8/18. https://doi.org/10.1109/IAS.2009.160

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author = "Akinori Ito and Akihito Aiba and Masashi Ito and Shozo Makino",

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Y1 - 2009/12/1

N2 - 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.

AB - 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.

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

Abstract

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