Semi-supervised Learning for Automatic Audio Events Annotation Using TSVM
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.
audio events (Aes) TSVM semi-supervised learning sample selection
Rongyan Wang Gang Liu Jun Guo Zhenxin Ma
Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, China
国际会议
太原
英文
530-534
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）