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
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.