A nested infinite Gaussian mixture model for identifying known and unknown audio events

This paper presents a novel statistical method that can classify given audio events into known classes or recognize them as an unknown class. We propose a nested infinite Gaussian mixture model (iGMM) to represent varied audio events in real environment. One of the main problems of conventional classification methods is that we need to specify a fixed number of classes in advance. Therefore, all audio events are forced to be classified into known classes. To solve the problem, the proposed method formulates a infinite Gaussian mixture model (iGMM) in which the number of classes are allowed to increase without bound. Another problem is that the complexity of each audio event is different. Then, the nested iGMM using nonparametric Bayesian approach is applied to adjust the needed dimension of each audio model. Experimental results show the effectiveness for these two problems to represent the given audio events.