Detection of Abnormal Sound Using Multi-stage GMM for Surveillance Microphone

摘要：

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.

作者: Akinori Ito Akihito Aiba Masashi Ito Shozo Makino

作者单位: Graduate School of Engineering,Tohoku University 6-6-05 Aramaki aza Aoba,Aoba-ku,Sendai 980-8579 Japan

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 733-736

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）