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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.02566 |
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| _version_ | 1866914630712950784 |
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| author | Li, Xiaoquan Weiss, Stephan Yan, Yijun Li, Yinhe Ren, Jinchang Soraghan, John Gong, Ming |
| author_facet | Li, Xiaoquan Weiss, Stephan Yan, Yijun Li, Yinhe Ren, Jinchang Soraghan, John Gong, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02566 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 Sound Machine Learning Multimedia Audio and Speech Processing 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. |
| title | Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment |
| topic | Sound Machine Learning Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2401.02566 |