Anomalous Sound Event Detection Based on One-Class Classification Using Variational Autoencoders and Interval Type-2 Fuzzy Sets
Zied Mnasri, Stefano Rovetta, Francesco Masulli · 2023
Audio information is increasingly being used by surveillance systems to improve their effectiveness. Thus, the present paper describes a novel method for detecting anomalous sound events in road traffic monitoring. To detect anomalies, the method combines generative variational autoencoders and interval type-2 fuzzy sets. The reconstruction error of each audio segment is computed using a baseline variational autoencoder, which offers a primary assessment of outlierness through thresholding, An interval type-2 fuzzy membership function with an optimistic/upper component and a pessimistic/lower component is employed to account for the uncertainty associated with this decision-making process. The final class attribution is made by interval comparison, based on a probabilistic technique. The evaluation results obtained after defuzzification reveal that the proposed membership function effectively enhances the performance of the baseline variational autoencoder.