Detecting emotional expression of music with feature selection approach

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

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%.

Original languageEnglish
Title of host publicationICOT 2013 - 1st International Conference on Orange Technologies
Pages282-286
Number of pages5
DOIs
Publication statusPublished - 2013 Jul 12
Event1st International Conference on Orange Technologies, ICOT 2013 - Tainan, Taiwan
Duration: 2013 Mar 122013 Mar 16

Publication series

NameICOT 2013 - 1st International Conference on Orange Technologies

Other

Other1st International Conference on Orange Technologies, ICOT 2013
CountryTaiwan
CityTainan
Period13-03-1213-03-16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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