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http://dx.doi.org/10.9717/kmms.2015.18.11.1358

Automatic Composition Using Training Capability of Artificial Neural Networks and Chord Progression  

Oh, Jin-Woo (Dept. of Information and Communications Engineering, Hansung University)
Song, Jung-Hyun (Dept. of Information and Communications Engineering, Hansung University)
Kim, Kyung-Hwan (Dept. of Information and Communications Engineering, Hansung University)
Jung, Sung Hoon (Dept. of Information and Communications Engineering, Hansung University)
Publication Information
Journal of Korea Multimedia Society / v.18, no.11, 2015 , pp. 1358-1366 More about this Journal
Abstract
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.
Keywords
Automatic Composition; Artificial Neural Networks; Chord Post-Processing;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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