An Approach of Genetic Programming for Music Emotion Classification
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- @Article{Bang:2013:IJCAS,
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author = "Sung-Woo Bang and Jaekwang Kim and Jee-Hyong Lee",
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title = "An Approach of Genetic Programming for Music Emotion
Classification",
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journal = "International Journal of Control, Automation and
Systems",
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year = "2013",
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volume = "11",
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number = "6",
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pages = "1290--1299",
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month = dec,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming,
Classification algorithm, emotion recognition, music
information retrieval",
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ISSN = "1598-6446",
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language = "English",
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DOI = "doi:10.1007/s12555-012-9407-7",
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size = "10 pages",
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abstract = "In this paper, we suggest a new approach of genetic
programming for music emotion classification. Our
approach is based on Thayer's arousal-valence plane
which is one of representative human emotion models.
Thayer's plane which says human emotions is determined
by the psychological arousal and valence. We map music
pieces onto the arousal-valence plane, and classify the
music emotion in that space. We extract 85 acoustic
features from music signals, rank those by the
information gain and choose the top k best features in
the feature selection process. In order to map music
pieces in the feature space onto the arousal-valence
space, we apply genetic programming. The genetic
programming is designed for finding an optimal formula
which maps given music pieces to the arousal-valence
space so that music emotions are effectively
classified. k-NN and SVM methods which are widely used
in classification are used for the classification of
music emotions in the arousal-valence space. For
verifying our method, we compare with other six
existing methods on the same music data set. With this
experiment, we confirm the proposed method is superior
to others.",
- }
Genetic Programming entries for
Sung-Woo Bang
Jaekwang Kim
Jee-Hyong Lee
Citations