Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment
Abstract
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, 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.
Keywords
Cite
@article{arxiv.2401.02566,
title = {Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment},
author = {Xiaoquan Li and Stephan Weiss and Yijun Yan and Yinhe Li and Jinchang Ren and John Soraghan and Ming Gong},
journal= {arXiv preprint arXiv:2401.02566},
year = {2024}
}
Comments
X.Li, S.Weiss, Y.Yan, Y.Li, J.Ren, J.Soraghan, M.Gong,"Siamese residual neural network for musical shape evaluation in piano performance assessment" in Proc. of the 31st European Signal Processing Conference, Helsinki, Finland