• Title/Summary/Keyword: 코드작곡 프로그램

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MIDI chord composition through chord generation program and automatic accompaniment program (조화로운 코드생성 프로그램과 자동반주 프로그램을 통한 미디 코드작곡)

  • 조재영;강희조;김윤호
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2004.05b
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    • pp.203-207
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    • 2004
  • This treatise presents that a possibility of non-musician's composition. In fart, as a development of music composition program(used by IAVA) helps to compose music easily, but non-musician still feels hard to compose some musics and perform some musics even though the band-in-a-box is already existed(which is an auto accompaniment program). This treatise shows non-musicians' special music composition way. Select a cord and put the cord to special program which ratted ‘the cord composition program’, and then the cord composition program creates the rest cords. After this progress, non-musicians overwrite their own melodies in accordance with created cords. Finally, they can perform their own musics with the sound of master-keyboard and other sound sources using by the band-in-a-box program.

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The embodiment of tree construction program for the composition by cord basis (코드기반 작곡을 위한 트리구조 프로그램 구현)

  • 조재영;김윤호;강희조;이명길
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2003.10a
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    • pp.272-276
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    • 2003
  • The appearance of the computer composition system(MIDI) expects that non-musicians can make musics easily, but, in fact, non-musicians still compose hard. This thesis shows that a computer makes lord progress through a database, which in-putted all practicable cord progress. In other words, non-musicians just makes some melodies over the settled cord progress, so they can make a music more easely.

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Analysis of Composition Chord Based on Back-propagation Neural Network (역전파 신경망을 이용한 작곡 코드 분석)

  • Jo Jae-Young;Kim Yoon-Ho;Lee Myung-kil
    • Journal of Digital Contents Society
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    • v.5 no.3
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    • pp.245-249
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    • 2004
  • This paper shows the reconstruction of existing composition chord program using back propagation neural network. In this approach, in order to produce the expectation values, weight values are controlled by neural network which rued chord pattern as a input vector. Experimental results showed that proposed approach is superior to a popular chord pattern method rather than existing composition program.

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