Automatic JAZZ improvisation using Genetic Programming and Long Short-Term Memory

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@Article{Ochi:2022:IPSJ,

• author = "Nanako Ochi and Kazuki Joe",
• title = "Automatic {JAZZ} improvisation using Genetic Programming and Long Short-Term Memory",
• journal = "IPSJ SIG Technical Report",
• year = "2022",
• volume = "2022",
• number = "16",
• month = "13 " # dec,
• note = "MPS-141",
• keywords = "genetic algorithms, genetic programming, Automatic composition, Long Short-Term Memory, LSTM, ANN, AI, music21",
• publisher = "Information Processing Society of Japan",
• URL = "https://ipsj.ixsq.nii.ac.jp/record/222798/files/IPSJ-MPS22141016.pdf",
• size = "6 pages",
• abstract = "In recent years, the use of participatory media by the ordinary people has become increasingly popular. This has led to an increase in demand for copyright-free music. However, it is difficult for ordinary users to compose music that requires musical expertise and experience. Especially, JAZZ improvisation is extremely difficult to reproduce due to the lack of musical examples. we propose a model for automatic generation of JAZZ improvisations using genetic programming and LSTM. In addition, we construct a system that automatically generates a musical score from the results of computer calculations, considering the usability for actual users.",
• notes = "In Japanese",

}

Genetic Programming entries for Nanako Ochi Joe Kazuki

Citations