{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T10:40:50Z","timestamp":1770979250822,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686462","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,12]]},"abstract":"<jats:p>This study proposes a novel method for quantitatively and visually representing individual preferences for piano performance styles. Such preferences are often highly subjective and difficult to articulate, thus limiting effective learning and the development of personalized artistic expressions. Using features such as tempo, dynamics, and their first- and second-order derivatives, and leveraging the Approximate Inverse Model Explanation (AIME) framework from Explainable AI (XAI), the method analyzes preference labels assigned by learners to professional performances, enabling the visualization of integrated, preference-based performance styles independent of specific pieces. This approach allows learners to explicitly visualize and verbalize their preferences, incorporate them into their performances, and identify professional performers whose stylistic tendencies align with their preferences. The experimental results demonstrate that the proposed method effectively captures integrated stylistic tendencies across multiple pieces and reveals stylistic similarities among performers that may remain hidden when using conventional numerical metrics alone. By making the abstract concept of performance style preferences concrete, the proposed method provides a systematic, explainable, and personalized framework that lays the groundwork for future studies aimed at bridging the gap between subjective musical preferences and actual learning outcomes, with potential applications in individualized music education, artistic self-discovery, and enriching musical performance culture.<\/jats:p>","DOI":"10.3233\/faia251724","type":"book-chapter","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T09:53:34Z","timestamp":1770976414000},"source":"Crossref","is-referenced-by-count":0,"title":["Proposed Visualization Method to Support Piano Learners Based on Performance Style Preferences"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4896-0692","authenticated-orcid":false,"given":"Ayako","family":"Minematsu","sequence":"first","affiliation":[{"name":"Graduate School of Data Science, Musashino University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0512-7644","authenticated-orcid":false,"given":"Takafumi","family":"Nakanishi","sequence":"additional","affiliation":[{"name":"School of Data Science, Tokyo University of Technology"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Information Modelling and Knowledge Bases XXXVII"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251724","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T09:53:35Z","timestamp":1770976415000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251724"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,12]]},"ISBN":["9781643686462"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251724","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,12]]}}}