“Music Structure Analysis based on Transformers”
Thursday March 9, 2023 | Time 14:00 CET
We present 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.
Masatoshi Hamanaka received his Ph.D. from the University of Tsukuba, Japan, in 2003. He is currently a leader of the Music Information Intelligence team on the Center for Advanced Intelligence Project, RIKEN. His research interests are music information technology, biomedical applications, and unmanned aircraft systems. He received the Journal of New Music Research Distinguished Paper Award in 2005, the SIGGRAPH2019 Emerging Technologies Laval Virtual Revolution Research Jury Prize in 2019, and the IJCAI-19 Most Entertaining Video Award in 2019.