# Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach

arXiv: https://arxiv.org/abs/2411.16408 · HTML: https://mlsanity.com/paper/2411.16408/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier](https://mlsanity.com/paper/2101.00562/) (2021)
- [Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription](https://mlsanity.com/paper/2402.07596/) (2024)
- [Optical Music Recognition: State of the Art and Major Challenges](https://mlsanity.com/paper/2006.07885/) (2020)
- [A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition](https://mlsanity.com/paper/2009.12577/) (2020)
- [DoReMi: First glance at a universal OMR dataset](https://mlsanity.com/paper/2107.07786/) (2021)
- [Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation](https://mlsanity.com/paper/2408.15002/) (2024)

## Built on by (most cited first, 1 of 1)

- [A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions](https://mlsanity.com/paper/2604.16446/) (2026)

## Abstract (arXiv)

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods. We overcome this challenge by training a neural-based feature extractor on unlabelled data, enabling effective classification with minimal samples. Key contributions include optimising crop preprocessing for a self-supervised Convolutional Neural Network and evaluating classification methods, including SVM, multilayer perceptrons, and prototypical networks. Our experiments yield an accuracy of 87.66%, showcasing the potential of AI-driven methods to ensure the survival of historical music for future generations through advanced digital archiving techniques.