Audio Analysis for Surveillance Applications


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.


  • Related News & Events

    •  NEWS    WASPAA 2005: 3 publications by Petros T. Boufounos, Ajay Divakaran and Paris Smaragdis
      Date: October 16, 2005
      Where: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
      MERL Contact: Petros T. Boufounos
      • The papers "Latent Variable Decomposition of Spectrograms for Single Channel Speaker Separation" by Raj, B. and Smaragdis, P., "Learning Source Trajectories Using Wrapped-Phase Hidden Markov Models" by Smaragdis, P. and Boufounos, P. and "Audio Analysis for Surveillance Applications" by Radhakrishnan, R., Divakaran, A. and Smaragdis, P. were presented at the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).