JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Rough Set-Based Approach for Automatic Emotion Classification of Music |
| 영문제목(English Title) |
Rough Set-Based Approach for Automatic Emotion Classification of Music |
| 저자(Author) |
Babu Kaji Baniya
Joonwhoan Lee
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| 원문수록처(Citation) |
VOL 13 NO. 02 PP. 0400 ~ 0416 (2017. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the 4th order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.
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| 키워드(Keyword) |
Attributes
Covariance
Discretize
Rough Set
Rules
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| 파일첨부 |
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