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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2016 Vol.6(2): 134-138 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.2.587

Music Genre Classification Using Feature Subset Search

Jihae Yoon, Hyunki Lim, and Dae-Won Kim
Abstract—With the growing number of digital music, the automatic genre recognition problem has been receiving the spotlight in music retrieval information field. A large number of musical acoustic features are reported to degrade the genre classification performance and lead to heavy computational cost. In this paper, we propose a new method for selecting genre-discriminative feature subset from a large number of musical features. We show that the proposed method is able to improve the genre recognition accuracy compared to the traditional selection method.

Index Terms—Genre classification, feature selection, mutual information, incremental search.

The authors are with the Chung-Ang University, Seoul, Korea (e-mail: jihae.cau@gmail.com, hyunki05@gmail.com, dwkim@cau.ac.kr).


Cite: Jihae Yoon, Hyunki Lim, and Dae-Won Kim, "Music Genre Classification Using Feature Subset Search," International Journal of Machine Learning and Computing vol.6, no. 2, pp. 134-138, 2016.

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