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Audio event classification, as an important part of Computational Auditory Scene Analysis, has attracted much attention. Currently, the classification technology is mature enough to classify isolated audio events accurately, but for overlapped audio events, it performs much worse. While in real life, most audio documents would have cer- tain percentage of overlaps, and so the overlap classifica- tion problem is an important part of audio classification. Nowadays, the work on overlapped audio event classifica- tion is still scarce, and most existing overlap classification systems can only recognize one audio event for an overlap. In this paper, in order to deal with overlaps, we innova- tively introduce the author-topic (AT) model which was first proposed for text analysis into audio classification, and innovatively combine it with PLSA (Probabilistic La- tent Semantic Analysis). We propose 4 systems, i.e. AT, PLSA, AT-PLSA and PLSA-AT, to classify overlaps. The 4 proposed systems have the ability to recognize two or more audio events for an overlap. The experimental results show that the 4 systems perform well in classifying overlapped audio events, whether it is the overlap in training set or the overlap out of training set. Also they perform well in clas- sifying isolated audio events.

Classification of Overlapped Audio Events Based on AT, PLSA, and the Combination of Them