Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment
This work addresses the time- and cost-intensive task of musical shape assessment for music education and performance evaluation, though it appears incremental as it applies a hybrid neural network approach to a specific domain.
The paper tackled the problem of automating musical shape evaluation in piano performance by proposing a Siamese residual neural network (S-ResNN) for classification, achieving significant improvements in precision, recall, and F1 score over benchmark methods.
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.