KSII Transactions on Internet and Information Systems (TIIS)

  • 제12권4호
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  • Pages.1869-1886
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  • 2018
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Korean Traditional Music Genre Classification Using Sample and MIDI Phrases

  • Lee, JongSeol (Department of Computer Science and Engineering Konkuk University) ;
  • Lee, MyeongChun (Smart Media R&D Korea Electronics Technology Institute) ;
  • Jang, Dalwon (Smart Media R&D Korea Electronics Technology Institute) ;
  • Yoon, Kyoungro (Department of Computer Science and Engineering Konkuk University)
  • 투고 : 2017.09.27
  • 심사 : 2017.03.08
  • 발행 : 2018.04.30
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초록

This paper proposes a MIDI- and audio-based music genre classification method for Korean traditional music. There are many traditional instruments in Korea, and most of the traditional songs played using the instruments have similar patterns and rhythms. Although music information processing such as music genre classification and audio melody extraction have been studied, most studies have focused on pop, jazz, rock, and other universal genres. There are few studies on Korean traditional music because of the lack of datasets. This paper analyzes raw audio and MIDI phrases in Korean traditional music, performed using Korean traditional musical instruments. The classified samples and MIDI, based on our classification system, will be used to construct a database or to implement our Kontakt-based instrument library. Thus, we can construct a management system for a Korean traditional music library using this classification system. Appropriate feature sets for raw audio and MIDI phrases are proposed and the classification results-based on machine learning algorithms such as support vector machine, multi-layer perception, decision tree, and random forest-are outlined in this paper.

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피인용 문헌

  1. DBTMPE: Deep Bidirectional Transformers-Based Masked Predictive Encoder Approach for Music Genre Classification vol.9, pp.5, 2018, https://doi.org/10.3390/math9050530