Abstract
This paper describes an analyzer that simultaneously learns grou** and metrical structures on the basis of the generative theory of tonal music (GTTM) by using a deep learning technique. GTTM is composed of four modules that are in series. GTTM has a feedback loop in which the former module uses the result of the latter module. However, as each module has been independent in previous GTTM analyzers, they did not form a feedback loop. For example, deepGTTM-I and deepGTTM-II independently learn grou** and metrical structures by using a deep learning technique. In light of this, we present deepGTTM-III, which is a new analyzer that includes the concept of feedback that enables simultaneous learning of grou** and metrical structures by integrating both deepGTTM-I and deepGTTM-II networks. The experimental results revealed that deepGTTM-III outperformed deepGTTM-I and had similar performance to deepGTTM-II.
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Acknowledgments
This work was supported by JSPS KAKENHI Grant Numbers 17H01847, 25700036, 16H01744, and 23500145.
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Hamanaka, M., Hirata, K., Tojo, S. (2018). deepGTTM-III: Multi-task Learning with Grou** and Metrical Structures. In: Aramaki, M., Davies , M., Kronland-Martinet, R., Ystad, S. (eds) Music Technology with Swing. CMMR 2017. Lecture Notes in Computer Science(), vol 11265. Springer, Cham. https://doi.org/10.1007/978-3-030-01692-0_17
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DOI: https://doi.org/10.1007/978-3-030-01692-0_17
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