Creation of Music Chord Progression Suited for User's Feelings Based on Interactive Genetic Algorithm

Music chord progression is one of musical elements that compose music piece. The music chord progression can be created freely by a user, however, combination of music chords is innumerable to define the progression. To resolve this problem, chord progression theory and basic patterns are often used. However, with these methods, it is hard for general users to create a music progression suited for each user's feelings. In order to create the chord progression suited for the user's feelings, user's own basic music knowledge and experience are required. In our previous study, we have proposed Interactive Genetic Algorithm (IGA) that creates music chord progression. In the IGA, Genetic Algorithm was used as evolutionary algorithm. In this study, through two listening experiments, we investigated the efficacy of the proposed IGA creating music chord progression. As the experiments, searching and evaluating experiments were conducted. As targets of creation of the chord progression, "bright" and "dark" chord progressions were selected as experimental conditions. In the result of the searching experiment, significant increases of fitness value were observed in both of the conditions. In the results of the evaluating experiment, in the 13th generation, large difference in impression between the conditions was observed.

[1]  Shigeki Sagayama,et al.  Hidden Markov Model Applied to Automatic Harmonization of Given Melodies , 2000 .

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[4]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Makoto Fukumoto,et al.  Generation of Appropriate User Chord Development Based on Interactive Genetic Algorithm , 2010, 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[7]  Makoto Fukumoto,et al.  The Efficiency of Interactive Differential Evolution in Creation of Sound Contents: In Comparison with Interactive Genetic Algorithm , 2012, 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

[8]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  Takehisa Onisawa,et al.  Music composition by interaction between human and computer , 2009, New Generation Computing.

[11]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[12]  J. M. Kittross The measurement of meaning , 1959 .