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One Class Support Vector Machines for audio abnormal events detection

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Abstract

This paper proposes an unsupervised method for real time detection of abnormal events in the context of audio surveillance. Based on training a One-Class Support Vector Machine (OC-SVM) to model the distribution of the normality (ambience), we propose to construct sets of decision functions. This allows controlling the trade-off between false-alarm and miss probabilities without modifying the trained OC-SVM that best capture the ambience boundaries, or its hyperparameters. Then we present an adaptive online scheme of temporal integration of the decision function output in order to increase performance and robustness. We also introduce a framework to generate databases based on real signals for the evaluation of audio surveillance systems. Finally, we present the performances obtained on the databases.

Dates and versions

hal-02356326 , version 1 (08-11-2019)

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Cite

Sebastien Lecomte, Régis Lengellé, Cédric Richard, Francois Capman. One Class Support Vector Machines for audio abnormal events detection. 2011 IEEE Statistical Signal Processing Workshop (SSP), Jun 2011, Nice, France. pp.489-492, ⟨10.1109/SSP.2011.5967739⟩. ⟨hal-02356326⟩
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