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.

翻译：理解与识别音乐形态在音乐教育与演奏评估中具有重要作用。为简化传统上耗时费力的音乐形态评估过程，本文探索如何应用基于人工智能（AI）的模型。本文将音乐形态评估视为分类问题，提出一种轻量级孪生残差神经网络（S-ResNN）来自动识别音乐形态。为评估所提方法在钢琴音乐形态评估中的效果，我们构建了一个新数据集，包含147首钢琴预备练习曲衍生出的4116个音乐片段，涵盖28类音乐形态。实验结果表明，S-ResNN在精确率、召回率和F1分数上均显著优于多种基准方法。