Acoustic Event Classification Using Ensemble of One-Class Classifiers for Monitoring Application

In this paper we investigate the application of ensemble of one-class classifiers to the problem of acoustic event classification. We present some initial results that are based on acoustic signals emitted by different litter causing material when contacted by human. When a person interacts with objects made with specific material, characteristic sounds are produced as a result of the interactions. We consider such interactions or activities as atomic events. We propose application of ensemble of one-class fuzzy rule-based classifiers to the problem of identification of activities that can cause possible litter in the public places. The experimental results show that the classifier gives satisfactory results and at the same time has low false alarm rate. The results are comparable to widely used one-class SVM. Moreover, the method is adaptive and suitable for incremental learning.