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

arXiv: https://arxiv.org/abs/2401.02566 · HTML: https://mlsanity.com/paper/2401.02566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MusPy: A Toolkit for Symbolic Music Generation](https://mlsanity.com/paper/2008.01951/) (2020)
- [An Overview of Deep Learning Architectures in Few-Shot Learning Domain](https://mlsanity.com/paper/2008.06365/) (2020)
- [Piano Skills Assessment](https://mlsanity.com/paper/2101.04884/) (2021)
- [An Interdisciplinary Review of Music Performance Analysis](https://mlsanity.com/paper/2104.09018/) (2021)

## Abstract (arXiv)

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.