Abstract
This paper describes a time-span tree leveled by the length of the time span. Using the time-span tree of the Generative Theory of Tonal Music, it is possible to reduce notes in a melody, but it is difficult to automate because the priority order of the branches to be reduced is not defined. A similar problem arises in the automation of time-span analysis and melodic morphing. Therefore, we propose a method for defining the priority order in total order in accordance with the length of the time span of each branch in a time-span tree. In the experiment, we confirmed that melodic morphing and deep learning of time-span tree analysis can be carried out automatically using the proposed method.
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This work was supported by JSPS KAKENHI Grant number 21H03572.
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Hamanaka, M., Hirata, K., Tojo, S. (2023). Time-Span Tree Leveled by Duration of Time-Span. In: Aramaki, M., Hirata, K., Kitahara, T., Kronland-Martinet, R., Ystad, S. (eds) Music in the AI Era. CMMR 2021. Lecture Notes in Computer Science, vol 13770 . Springer, Cham. https://doi.org/10.1007/978-3-031-35382-6_5
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DOI: https://doi.org/10.1007/978-3-031-35382-6_5
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