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arXiv · 2411.16408

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

Elona Shatri·Daniel Raymond·George Fazekas
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Abstract

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 Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods.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.

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Elona Shatri, Daniel Raymond, George Fazekas. 2024-11-25. Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach. https://arxiv.org/abs/2411.16408

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