  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.
