Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913585249124352 |
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| author | Shatri, Elona Raymond, Daniel Fazekas, George |
| author_facet | Shatri, Elona Raymond, Daniel Fazekas, George |
| contents | 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 Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods.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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_16408 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach Shatri, Elona Raymond, Daniel Fazekas, George Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition 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 Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods.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. |
| title | Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach |
| topic | Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.16408 |