@techreport{oai:ipsj.ixsq.nii.ac.jp:00224331,
 author = {吉井, 悠馬 and 橋田, 光代 and 片寄, 晴弘 and Yuma, Yoshii and Mitsuyo, Hashida and Haruhiro, Katayose},
 issue = {7},
 month = {Feb},
 note = {深層学習の進展により演奏の表情付けの研究は大きく進展した．その一方で，演奏者の表現意図が演奏制御情報としてどう展開されるのかについてはよくわかっていないことも多い．我々は，ピアニスト本人が解したフレーズ構造情報に着目し，それが演奏制御情報としてどう変換されるのかについて重回帰分析を用いた分析を進めている．本稿では，音量，テンポ，タッチを対象とした際の分析状況について報告を行う．, Research on performance expression has progressed greatly with the development of deep learning. On the other hand, how the performer's expressive intention is developed as performance control information is often not well understood. We have focused on the phrase structure information that the pianist has solved and have been analyzing how it is converted into performance control information using multiple regression analysis. In this paper, we report on the analysis of volume, tempo, and touch.},
 title = {ピアニストの楽曲構造解釈と演奏制御の関係の解析に向けての予備検討},
 year = {2023}
}