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arXiv · 2401.02566

Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment

Xiaoquan Li·Stephan Weiss·Yijun Yan·Yinhe Li·Jinchang Ren·John Soraghan·Ming Gong
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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.

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Xiaoquan Li, Stephan Weiss, Yijun Yan, Yinhe Li, Jinchang Ren, John Soraghan, Ming Gong. 2024-01-04. Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment. https://arxiv.org/abs/2401.02566

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