A multi-objective evolutionary approach to automatic melody generation |
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| Affiliation: | 1. Department of Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 440-746, Republic of Korea;2. School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Gwangju 61005, Republic of Korea;1. Laboratório de Inteligência Computacional do Araripe, Instituto Federal do Sertão Parnambucano, Brazil;2. Centro de Informática, Universidade Federal de Pernambuco, Brazil;1. Universidad de Alcalá, 28871 Alcalá de Henares Madrid, Spain;2. Universidade de Santiago de Compostela, Santiago de Compostela Galicia E-15782, Spain;1. School of Engineering, University of South Australia, Mawson Lakes Campus, Mawson Lakes, South Australia 5095, Australia;2. The Logistics Institute – Asia Pacific, National University of Singapore, 21 Heng Mui Keng Terrace, Singapore;1. Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510006, China;2. Knowledge Management and Innovation Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 999077, China;1. Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong;2. Department of Mathematics, Chinese University of Hong Kong, Shatin, Hong Kong |
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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. |
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