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1st International Workshop on Reading Music Systems

[WORMS]

Paris, September 20

PRAIg '18 / +pics

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11th international workshop on Machine Learning and Music

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Stockholm: 13-15th July 2018

Publications:

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  1. Piccoli, H. C. B.; Silla, C. N. Jr.; Ponce de León, P. J.; Pertusa, A.
    "An Evaluation of Symbolic Feature Sets and Their Combination for Music Genre Classification"
    Proc. of the 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC), ISBN: 978-0-7695-5154-8, pp. 1901-1905, Manchester, UK (2013)
    : bibtex : pdf : IEE Xplore page
    Abstract:

    The automatic music genre classification task is an active area of research in the field of Music Information Retrieval. In this paper we use two different symbolic feature sets for genre classification and combine them using an early fusion approach. Our results show that early fusion achieves better classification accuracy than using any of the individual feature sets. Furthermore, when compared with some of the state of the art approaches using the same experimental conditions, early fusion of symbolic features is ranked the second best method.

@inproceedings {
 author = "Piccoli, H. C. B.; Silla, C. N. Jr.; Ponce de León, P. J.; Pertusa, A.",
 title  = "An Evaluation of Symbolic Feature Sets and Their Combination for Music Genre Classification",
 address = "Manchester, UK",
 booktitle = "Proc. of the 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC)",
 isbn = "978-0-7695-5154-8",
 month = "October",
 pages = "1901-1905",
 publisher = "IEEE Computer Society",
 year = "2013"
}
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