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Siamese residual neural network for musical shape evaluation in piano performance assessment.

Li, Xiaoquan; Weiss, Stephan; Yan, Yijun; Li, Yinhe; Ren, Jinchang; Soraghan, John; Gong, Ming


Xiaoquan Li

Stephan Weiss

Yijun Yan

John Soraghan

Ming Gong


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.


LI, X., WEISS, S., YAN, Y., LI, Y., REN, J., SORAGHAN, J. and GONG, M. 2023. Siamese residual neural network for musical shape evaluation in piano performance assessment. In Proceedings of the 31st European signal processing conference 2023 (EUSIPCO 2023), 4-8 September 2023, Helsinki, Finland. Piscataway: IEEE [online], pages 216-220. Available from:

Conference Name 31st European signal processing conference 2023 (EUSIPCO 2023)
Conference Location Helsinki, Finland
Start Date Sep 4, 2023
End Date Sep 8, 2023
Acceptance Date Nov 1, 2023
Online Publication Date Nov 1, 2023
Publication Date Dec 31, 2023
Deposit Date Dec 15, 2023
Publicly Available Date Dec 15, 2023
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Pages 216-220
Series Title European signal processing conference (EUSIPCO)
Series ISSN 2076-1465
ISBN 9789464593600
Keywords Piano performance assessment; Audio classification; Musical shape evaluation; Siamese network
Public URL


LI 2023 Siamese residual neural network (AAM) (704 Kb)

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