In this paper an acoustic surveillance method is proposed for accurately detecting hazardous situations under noisy conditions. In order to improve detection accuracy, the proposed method first tries to separate each atypical event from the input noisy audio signal. Next, maximum likelihood classification using multiple hidden Markov models (HMMs) is carried out to decide whether or not an atypical event occurs. Performance evaluation shows that the proposed method achieves higher detection accuracy under various signal-to-noise ratio (SNR) conditions than a conventional HMM-based method.

Authors: Jeon, Kwang Myung; Lee, Dong Yun; Kim, Hong Kook; Lee, Myung J.
Affiliations: Gwangju Institute of Science and Technology (GIST), Gwangju, Korea; City University of New York, New York, NY, USA(See document for exact affiliation information.)
AES Convention: 137 (October 2014) Paper Number: 9203
Publication Date: October 8, 2014
Subject: Applications in Audio

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