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.