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

Research output: Contribution to conferencePaperpeer-review

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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.
Original languageEnglish
Number of pages5
Publication statusE-pub ahead of print - 4 Sept 2023
Event31st European Signal Processing Conference - Helsinki, Finland
Duration: 4 Sept 20238 Sept 2023


Conference31st European Signal Processing Conference
Abbreviated titleEUSIPCO'23
Internet address


  • piano performance assessment
  • audio classification
  • musical shape evaluation
  • Siamese network
  • music information retrieval
  • MPA


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