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Music Emotion Classification Based On Three-Level Structure  

Kim, Hyoung-Gook (Intelligent Multimedia Signal Processing, Kwangwoon University)
Jeong, Jin-Guk (Samsung Advanced Institute of Technology)
Publication Information
The Journal of the Acoustical Society of Korea / v.26, no.2E, 2007 , pp. 56-62 More about this Journal
Abstract
This paper presents the automatic music emotion classification on acoustic data. A three-level structure is developed. The low-level extracts the timbre and rhythm features. The middle-level estimates the indication functions that represent the emotion probability of a single analysis unit. The high-level predicts the emotion result based on the indication function values. Experiments are carried out on 695 homogeneous music pieces labeled with four emotions, including pleasant, calm, sad, and excited. Three machine learning methods, GMM, MLP, and SVM, are compared on the high-level. The best result of 90.16% is obtained by MLP method.
Keywords
automatic music emotion classification; timbre and rhythm features; machine learning methods;
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