On the Joint Use of Nmf and Classification for Overlapping Acoustic Event Detection

In this paper, we investigate the performance of classifierbased non-negative matrix factorization (NMF) methods for detecting overlapping acoustic events. We provide evidence that the performance of classifier-based NMF systems deteriorates significantly in overlapped scenarios in case mixed observations are unavailable during training. To this end, we propose a K-means based method for artificial generation of mixed data. The method of Mixture of Local Dictionaries (MLD) is employed for the building of the NMF dictionary using both the isolated and artificially mixed data. Finally an SVM classifier is trained for each of the isolated and mixed event classes, using the corresponding MLD-NMF activations from the training set. The proposed system, tested on two experiments with a) synthetic and b) real events, outperforms the state-of-the-art classifier-based NMF system in the overlapped scenarios.