TY - GEN
T1 - Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment
AU - Li, Xiaoquan
AU - Weiss, Stephan
AU - Yan, Yijun
AU - Li, Yinhe
AU - Ren, Jinchang
AU - Soraghan, John
AU - Gong, Ming
N1 - Copyright:
© 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - Understanding and identifying musical shape plays an important role in music education and performance assessment. To simplify the otherwise time- and cost-intensive musical shape evaluation, in this paper we explore how artificial intelligence (AI) driven models can be applied. Considering musical shape evaluation as a classification problem, a light-weight Siamese residual neural network (S-ResNN) is proposed to automatically identify musical shapes. To assess the proposed approach in the context of piano musical shape evaluation, we have generated a new dataset, namely MSED-4k, containing 4116 music pieces derived by 147 piano preparatory exercises and performed in 28 categories of musical shapes. The experimental results show that the S-ResNN significantly outperforms a number of benchmark methods in terms of the precision, recall and F1 score.
AB - Understanding and identifying musical shape plays an important role in music education and performance assessment. To simplify the otherwise time- and cost-intensive musical shape evaluation, in this paper we explore how artificial intelligence (AI) driven models can be applied. Considering musical shape evaluation as a classification problem, a light-weight Siamese residual neural network (S-ResNN) is proposed to automatically identify musical shapes. To assess the proposed approach in the context of piano musical shape evaluation, we have generated a new dataset, namely MSED-4k, containing 4116 music pieces derived by 147 piano preparatory exercises and performed in 28 categories of musical shapes. The experimental results show that the S-ResNN significantly outperforms a number of benchmark methods in terms of the precision, recall and F1 score.
KW - Audio Classification
KW - Musical Shape Evaluation
KW - Piano Performance Assessment
KW - Siamese Network
UR - http://www.scopus.com/inward/record.url?scp=85178370290&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO58844.2023.10289901
DO - 10.23919/EUSIPCO58844.2023.10289901
M3 - Conference contribution
AN - SCOPUS:85178370290
SN - 9798350328110
T3 - European Signal Processing Conference
SP - 216
EP - 220
BT - 3st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
PB - IEEE
T2 - 31st European Signal Processing Conference, EUSIPCO 2023
Y2 - 4 September 2023 through 8 September 2023
ER -