Anomaly detection based on interval-valued fuzzy sets: Application to rare sound event detection

Audio signal processing is moving towards detecting and/or defining rare/anomalous sounds. The ap- plication of such an anomaly detection problem can be easily extended to audio surveillance systems. Thus, a rare sound event detection method for road traffic monitoring is proposed in this paper, includ- ing detection of hazardous events, i.e., road accidents. The method is based on combining anomaly detection techniques, such as variational autoencoders (VAE) and Interval-valued fuzzy sets. The VAE is used to calculate the reconstruction error of the input audio segment. Based on this reconstruction er- ror, a fuzzy membership function, composed of an optimistic/upper component and a pessimistic/lower component, is calculated. Finally, a probabilistic method for interval comparison is used to calculate the membership score, hence to evaluate the interval-valued fuzzy sets. Finally, classification into anoma- lous/normal events is obtained by defuzzification. Results show that with a careful parameter setting, the proposed method outperforms the state-of-the-art one-class SVM for anomaly detection.