We propose a time series analysis based approach for systematic choice of audio classes for detection of crimes in elevators in 1. Since all the different sounds in a surveillance environment cannot be anticipated, a surveillance system for event detection cannot complete rely on a supervised audio classification framework. In this paper, we propose a hybrid solution that consists two parts; one that performs unsupervised audio analysis and another that performs analysis using an audio classification framework obtained from off-line analysis and training. The proposed system is capable of detecting new kinds of suspicious audio events that occur as outliers agains a background of usual activity. It adaptively learns a Gaussian Mixture Model (GMM) to model the background sounds and updates the model incrementally as new audio data arrives. New types of suspicious events can be detected as deviants from this usual background model. The results on elevator audio data are promising. WASPAA 2005This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. AUDIO ANALYSIS FOR SURVEILLANCE APPLICATIONS Regunathan Radhakrishnan, Ajay Divakaran and Paris SmaragdisMitsubishi Electric Research Labs 201 Broadway Cambridge, MA, USA regu@merl.com, ajayd@merl.com, paris@merl.com ABSTRACTWe proposed a time series analysis based approach for systematic choice of audio classes for detection of crimes in elevators in [1]. Since all the different sounds in a surveillance environment cannot be anticipated, a surveillance system for event detection cannot completely rely on a supervised audio classification framework. In this paper, we propose a hybrid solution that consists two parts; one that performs unsupervised audio analysis and another that performs analysis using an audio classification framework obtained from off-line analysis and training. The proposed system is capable of detecting new kinds of suspicious audio events that occur as outliers against a background of usual activity. It adaptively learns a Gaussian Mixture Model(GMM) to model the background sounds and updates the model incrementally as new audio data arrives. New types of suspicious events can be detected as deviants from this usual background model. The results on elevator audio data are promising.
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