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CROCUS: Dataset of Critique Documents of Musical Performance

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Music in the AI Era (CMMR 2021)

Abstract

In performance education, verbal as well as nonverbal information is used to convey knowledge. In the COVID-19 social situation, the demand for remote and asynchronous lessons is increasing, and it is unclear what kind of verbal information should be used. In this study, we collected 239 Japanese review texts from 12 teachers for a total of 90 orchestral studies performed by oboe players. We categorized the sentences of the critiques, and found that the content of the critiques varied more by teacher than by piece or student. We also found that the category of giving practice strategy played a significant role in the content of instruction that students found useful.

M. Matsubara, R. Kagawa—Two authors equally contributed to this research.

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Notes

  1. 1.

    A part of this manuscript has been presented at the 15th International Symposium on Computer Music Multidisciplinary Research (CMMR 2021) [24], with additional results in “Evaluation of the validity of the classification” subsection in Sect. 3.2, parts of Sect. 4.3, Fig. 7, and regarding references.

  2. 2.

    Dataset is public on https://doi.org/10.5281/zenodo.4748243.

  3. 3.

    Types of “Demonstrating”, “Modelling”, and “Listening/Observing” were omitted because these actions are not observed in textual critique.

  4. 4.

    For one critique was missed during collecting process.

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Acknowledgment

This study was partially supported by JST-Mirai Program Grant Number JPMJMI19G8, and JSPS KAKENHI Grant Number JP19K19347. We would like to thank all the performers and teachers who participated in the data collection of this study. We would also thank to those who helped us with data annotation and evaluation.

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Correspondence to Masaki Matsubara .

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Matsubara, M., Kagawa, R., Hirano, T., Tsuji, I. (2023). CROCUS: Dataset of Critique Documents of Musical Performance. 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_20

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  • DOI: https://doi.org/10.1007/978-3-031-35382-6_20

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