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Understanding and identifying musical shape plays an important role in musiceducation and performance assessment. To simplify the otherwise time- andcost-intensive musical shape evaluation, in this paper we explore howartificial intelligence (AI) driven models can be applied. Considering musicalshape evaluation as a classification problem, a light-weight Siamese residualneural network (S-ResNN) is proposed to automatically identify musical shapes.To assess the proposed approach in the context of piano musical shapeevaluation, we have generated a new dataset, containing 4116 music piecesderived by 147 piano preparatory exercises and performed in 28 categories ofmusical shapes. The experimental results show that the S-ResNN significantlyoutperforms a number of benchmark methods in terms of the precision, recall andF1 score.

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