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http://dx.doi.org/10.9723/jksiis.2013.18.4.025

Music Mood Classification based on a New Feature Reduction Method and Modular Neural Network  

Song, Min Kyun (금오공과대학교 컴퓨터소프트웨어공학과)
Kim, HyunSoo (금오공과대학교 컴퓨터소프트웨어공학과)
Moon, Chang-Bae (금오공과대학교 컴퓨터소프트웨어공학과)
Kim, Byeong Man (금오공과대학교 컴퓨터소프트웨어공학과)
Oh, Dukhwan (금오공과대학교 컴퓨터소프트웨어공학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.18, no.4, 2013 , pp. 25-35 More about this Journal
Abstract
This paper focuses on building a generalized mood classification model with many mood classes instead of a personalized one with few mood classes. Two methods are adopted to improve the performance of mood classification. The one of them is feature reduction based on standard deviation of feature values, which is designed to solve the problem of lowered performance when all 391 features provided by MIR toolbox used to extract features of music. The experiments show that the feature reduction methods suggested in this paper have better performance than that of the conventional dimension reduction methods, R-Square and PCA. As performance improvement by feature reduction only is subject to limit, modular neural network is used as another method to improve the performance. The experiments show that the method also improves performance effectively.
Keywords
Mood Classification; Feature Dimension Reduction; Modular Neural Network;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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