Detection of Hazardous Road Events From Audio Streams: An Ensemble Outlier Detection Approach

Abstract

Change detection in audio data streams has become an efficient way for online notification of events. An interesting application is audio surveillance, including road traffic monitoring and online car crash alarms. However, in the particular case of crash alarms, most of the collected sounds are background noises, with various sub-classes, such as talking pedestrians, engine sounds, horn blowing, etc., whereas those corresponding to the event of interest are much less abundant. Therefore, it is difficult to apply classical tools of classification or clustering to detect crash sounds as a particular class or as outliers. To tackle this problem, we propose an ensemble classifier based on one-class SVM in order to separate outliers from normal data first, and deep neural networks to classify event-related data. Finally the results of both outlier detection and classification outputs are aggregated in such a way that outliers are considered as a novel class, a priori unknown by the DNN classifier. The application of this method on an audio traffic monitoring database confirms its ability to detect, (a) non-events (background noise), nonhazardous and hazardous event, and (b) non-accidents and accidents, from a stream of audio data.