Abstract
In this study, we automate the production of good-quality jazz melodies through genetic algorithm and pattern learning by preserving the musically important properties. Unlike previous automatic composition studies that use fixed-length chromosomes to express a bar in a score, we use a variable-length chromosome and geometric crossover to accommodate the variable length. Pattern learning uses the musical instrument digital interface data containing the jazz melody; a user can additionally learn about the melody pattern by scoring the generated melody. The pattern of the music is stored in a chord table that contains the harmonic elements of the melody. In addition, a sequence table preserves the flow and rhythmic elements. In the evaluation function, the two tables are used to calculate the fitness of a given excerpt. We use this estimated fitness and geometric crossover to improve the music until users are satisfied. Through this, we successfully create a jazz melody as per user preference and training data.
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Acknowledgement
We would like to thank Prof. Francisco Fernández de Vega for providing us with much advice and help in writing this paper. This research was supported by a grant [KCG-01-2017-05] through the Disaster and Safety Management Institute funded by Korea Coast Guard of Korean government, and it was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2015R1D1A1A01060105).
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Nam, YW., Kim, YH. (2019). Automatic Jazz Melody Composition Through a Learning-Based Genetic Algorithm. In: Ekárt, A., Liapis, A., Castro Pena, M.L. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2019. Lecture Notes in Computer Science(), vol 11453. Springer, Cham. https://doi.org/10.1007/978-3-030-16667-0_15
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DOI: https://doi.org/10.1007/978-3-030-16667-0_15
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