An Approach of Genetic Programming for Music Emotion Classification

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  author =       "Sung-Woo Bang and Jaekwang Kim and Jee-Hyong Lee",
  title =        "An Approach of Genetic Programming for Music Emotion
  journal =      "International Journal of Control, Automation and
  year =         "2013",
  volume =       "11",
  number =       "6",
  pages =        "1290--1299",
  month =        dec,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming,
                 Classification algorithm, emotion recognition, music
                 information retrieval",
  ISSN =         "1598-6446",
  language =     "English",
  DOI =          "doi:10.1007/s12555-012-9407-7",
  size =         "10 pages",
  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