@techreport{oai:ipsj.ixsq.nii.ac.jp:00232500,
 author = {李, 森浩 and 松野, 裕 and Senhao, Li and Yutaka, Matsuno},
 issue = {30},
 month = {Feb},
 note = {Classical music continues to captivate audiences worldwide, and advancements in automatic piano playing technologies and the Internet allows people to enjoy professional piano performances at home. However, the labor-intensive process of recording tracks on sensor-equipped pianos and the limited repertoire available present challenges. Thus, in this paper, we propose a neural network–based method to generate automatic piano performances. The proposed method utilizes long short-term memory (LSTM) networks, designed for the specific pianist, and contributes to the field of music notation learning. We employ LSTM networks rather than a recurrent neural network to decipher and encapsulate the intricate temporal dependencies characteristic of piano performances. We conducted training loss evaluation and pianist perception assessment. Experimental outcomes demonstrated predictive accuracy with R-squared (R2) values between 0.4 and 0.6. Blind listening tests revealed the model’s effectiveness in generating performances that are comparable to those of skilled pianists., Classical music continues to captivate audiences worldwide, and advancements in automatic piano playing technologies and the Internet allows people to enjoy professional piano performances at home. However, the labor-intensive process of recording tracks on sensor-equipped pianos and the limited repertoire available present challenges. Thus, in this paper, we propose a neural network–based method to generate automatic piano performances. The proposed method utilizes long short-term memory (LSTM) networks, designed for the specific pianist, and contributes to the field of music notation learning. We employ LSTM networks rather than a recurrent neural network to decipher and encapsulate the intricate temporal dependencies characteristic of piano performances. We conducted training loss evaluation and pianist perception assessment. Experimental outcomes demonstrated predictive accuracy with R-squared (R2) values between 0.4 and 0.6. Blind listening tests revealed the model’s effectiveness in generating performances that are comparable to those of skilled pianists.},
 title = {LSTM 特徴抽出と楽譜分析による特定のピアニストの演奏スタイルを模倣する自動演奏に関する研究},
 year = {2024}
}