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A multi-objective evolutionary approach to automatic melody generation

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Abstract
Existing evolutionary approaches to automatic composition generate only a few melodies in a certain style that is specified by the setting of parameters or the design of fitness functions. Thus, their composition results cannot cover the various tastes of music. In addition, they are not able to deal with the multidimensional nature of music. This paper presents a novel multi-objective evolutionary approach to automatic melody composition in order to produce a variety of melodies at once. To this end, two conflicting fitness measures are investigated to evaluate the fitness of melody; (1) stability and (2) tension. Resorting to music theory, genetic operators (i.e., crossover and mutation) are newly designed to improve search capability in the multi-objective fitness space of music composition. The experimental results demonstrate the validity and effectiveness of the proposed approach. Moreover, the analysis of composition results proves that the proposed approach generates a set of pleasant and diverse melodies. (C) 2017 Elsevier Ltd. All rights reserved.
Author(s)
Jeong, JaehunKim, YusungAhn, Chang Wook
Issued Date
2017-12
Type
Article
DOI
10.1016/j.eswa.2017.08.014
URI
https://scholar.gist.ac.kr/handle/local/13483
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
Expert Systems with Applications, v.90, pp.50 - 61
ISSN
0957-4174
Appears in Collections:
Department of AI Convergence > 1. Journal Articles
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