Few-Shot Music Symbol Classification via Self-Supervised Learning and Nearest Neighbor
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Campo DC | Valor | Idioma |
---|---|---|
dc.contributor | Reconocimiento de Formas e Inteligencia Artificial | es_ES |
dc.contributor.author | Alfaro-Contreras, María | - |
dc.contributor.other | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | es_ES |
dc.date.accessioned | 2023-11-15T12:46:35Z | - |
dc.date.available | 2023-11-15T12:46:35Z | - |
dc.date.issued | 2023-11 | - |
dc.identifier.citation | Alfaro-Contreras, María. “Few-Shot Music Symbol Classification via Self-Supervised Learning and Nearest Neighbor”. In: Calvo-Zaragoza, Jorge; Pacha, Alexander; Shatri, Elona (Eds.). Proceedings of the 5th International Workshop on Reading Music Systems: 4th November, 2023, Milan, Italy, pp. 39-43 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10045/138494 | - |
dc.description.abstract | The recognition of music symbols within score images represents one of the main stages in Optical Music Recognition systems. While current state-of-the-art methods based on Deep Learning are capable of adequately performing this task, they generally require a vast amount of data that has to be manually labeled. Such a particularity generally limits their applicability when addressing historical manuscripts with early music notation, for which annotated data is considerably scarce. In this paper, we propose a self-supervised learning-based method that addresses this task by training a neural-based feature extractor with a set of unlabeled documents and performs the recognition task considering just a few reference samples. Experiments on a reference early music corpus report that the proposal outperforms the contemplated baseline strategies even with a remarkably reduced number of labeled examples for the classification task. | es_ES |
dc.description.sponsorship | This paper is part of the project I+D+i PID2020-118447RA-I00, funded by MCIN/AEI/10.13039/501100011033. The author is supported by grant FPU19/04957 from the Spanish Ministerio de Universidades. | es_ES |
dc.language | eng | es_ES |
dc.publisher | International Workshop on Reading Music Systems | es_ES |
dc.rights | © The respective authors. Licensed under a Creative Commons Attribution 4.0 International License (CC-BY-4.0). | es_ES |
dc.subject | Music Symbol Classification | es_ES |
dc.subject | Optical Music Recognition | es_ES |
dc.subject | Self-Supervised Learning | es_ES |
dc.subject | Few-shot Learning | es_ES |
dc.title | Few-Shot Music Symbol Classification via Self-Supervised Learning and Nearest Neighbor | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.peerreviewed | si | es_ES |
dc.relation.publisherversion | https://doi.org/10.48550/arXiv.2311.04091 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118447RA-I00 | es_ES |
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Alfaro-Contreras_Proceedings-5th-International-Workshop-on-Reading-Music-Systems.pdf | 1,05 MB | Adobe PDF | Abrir Vista previa | |
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