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Semi-supervised learning for automatic audio events annotation using TSVM

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4 Author(s)
Rongyan Wang ; Pattern Recognition & Intell. Syst. Lab., Beijing Univ. of Posts & Telecommun., Beijing, China ; Gang Liu ; Jun Guo ; Zhenxin Ma

Most previous approaches to automatic audio events (AEs) annotation are based on supervised learning which relies on the availability of a labeled corpus to train classification models. However, instance annotation is often difficult, expensive, and time consuming. In this paper, we apply semi-supervised learning with transductive Support Vector Machine (TSVM) algorithm to automatic AEs annotation. Besides, considering about the presence of outliers which degrade the generalization and the classification performance, we propose a confidence-based method for samples selection. In our experiments based on the melodrama Friends corpus, the proposed method can effectively use unlabeled data to improve the classification performance with only a small amount of the labeled data.

Published in:

Computer Application and System Modeling (ICCASM), 2010 International Conference on  (Volume:4 )

Date of Conference:

22-24 Oct. 2010