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Automatic Composition Using Training Capability of Artificial Neural Networks and Chord Progression
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 Title & Authors
Automatic Composition Using Training Capability of Artificial Neural Networks and Chord Progression
Oh, Jin-Woo; Song, Jung-Hyun; Kim, Kyung-Hwan; Jung, Sung Hoon;
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This paper proposes an automatic composition method using the training capability of artificial neural networks and chord progression rules that are widely used by human composers. After training a given song, the new melody is generated by the trained artificial neural networks through applying a different initial melody to the neural networks. The generated melody should be modified to fit the rhythm and chord progression rules for generating natural melody. In order to achieve this object, we devised a post-processing method such as chord candidate generation, chord progression, and melody correction. From some tests we could find that the melody after the post-processing was very improved from the melody generated by artificial neural networks. This enables our composition system to generate a melody which is similar to those generated by human composers.
Automatic Composition;Artificial Neural Networks;Chord Post-Processing;
 Cited by
자동작곡에서 조성과 반복구성을 위한 후처리 방법 및 다수 곡 학습을 위한 평균 신경망 방법,김경환;정성훈;

한국지능시스템학회논문지, 2016. vol.26. 6, pp.445-451 crossref(new window)
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디지털콘텐츠학회 논문지, 2016. vol.17. 6, pp.449-459 crossref(new window)
Adoption of Artificial Neural Network for Rest, Enhanced Postprocessing of Beats, and Initial Melody Processing for Automatic Composition System, Journal of Digital Contents Society, 2016, 17, 6, 449  crossref(new windwow)
Postprocessing for Tonality and Repeatability, and Average Neural Networks for Training Multiple Songs in Automatic Composition, Journal of Korean Institute of Intelligent Systems, 2016, 26, 6, 445  crossref(new windwow)
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