Dealing with Uncertainty in Anomalous Audio Event Detection Using Fuzzy Modeling

Abstract

Surveillance systems are getting more and more multimodal. The availability of audio motivates a method for anomalous audio event detection (anomalous AED) for road traffic surveillance, which is proposed in this paper. The method is based on combining anomaly detection techniques, such as reconstruction deep autoencoders and fuzzy membership functions. A baseline deep autoencoder is used to compute the reconstruction error of each audio segment. The comparison of this error to a preset threshold provides a primary estimation of outlierness. To account for the uncertainty associated to this decision-making step, a fuzzy membership function composed of an optimistic/upper component and a pessimistic/lower component is used. Evaluation results obtained after defuzzification show that with a careful parameter setting, the proposed membership function improves the performance of the baseline autoencoder for anomaly detection, and yields better or at least similar results than other anomaly detection state-of-the-art methods such as one-class SVM.