Detecting emotional expression of music with feature selection approach

Fang Chen Hwang, Jeen-Shing Wang, Pau-Choo Chung, Ching Fang Yang

研究成果: Conference contribution

7 引文 斯高帕斯(Scopus)

摘要

This paper presents a mechanism on detecting emotional expression of music with feature selection approach. Happiness, sadness, anger, and peace are considered in the classification problem. The thirty-seven features were extracted to represent the characteristics of music samples, such as rhythm, dynamic, pitch, and timbre features. The kernel-based class separability (KBCS) was introduced to prioritize features for emotion classification because not all features have the same importance in achieving emotional expression. Two feature transformation techniques, principal component analysis (PCA) and linear discriminant analysis (LDA) were applied after the feature selection. The inclusion of these two techniques can effectively improve the classification accuracy. To the end, the k-nearest neighborhood (k-NN) classifier is adopted. The results indicate that the proposed method in the study can achieve accuracy at almost 90%.

原文English
主出版物標題ICOT 2013 - 1st International Conference on Orange Technologies
頁面282-286
頁數5
DOIs
出版狀態Published - 2013 七月 12
事件1st International Conference on Orange Technologies, ICOT 2013 - Tainan, Taiwan
持續時間: 2013 三月 122013 三月 16

出版系列

名字ICOT 2013 - 1st International Conference on Orange Technologies

Other

Other1st International Conference on Orange Technologies, ICOT 2013
國家Taiwan
城市Tainan
期間13-03-1213-03-16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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