Few-Shot Music Symbol Classification via Self-Supervised Learning and Nearest Neighbor
Empreu sempre aquest identificador per citar o enllaçar aquest ítem
http://hdl.handle.net/10045/138494
Títol: | Few-Shot Music Symbol Classification via Self-Supervised Learning and Nearest Neighbor |
---|---|
Autors: | Alfaro-Contreras, María |
Grups d'investigació o GITE: | Reconocimiento de Formas e Inteligencia Artificial |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Paraules clau: | Music Symbol Classification | Optical Music Recognition | Self-Supervised Learning | Few-shot Learning |
Data de publicació: | de novembre-2023 |
Editor: | International Workshop on Reading Music Systems |
Citació bibliogràfica: | 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 |
Resum: | 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. |
Patrocinadors: | 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. |
URI: | http://hdl.handle.net/10045/138494 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/conferenceObject |
Drets: | © The respective authors. Licensed under a Creative Commons Attribution 4.0 International License (CC-BY-4.0). |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.48550/arXiv.2311.04091 |
Apareix a la col·lecció: | INV - GRFIA - Comunicaciones a Congresos, Conferencias, etc. |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
---|---|---|---|---|
Alfaro-Contreras_Proceedings-5th-International-Workshop-on-Reading-Music-Systems.pdf | 1,05 MB | Adobe PDF | Obrir Vista prèvia | |
Tots els documents dipositats a RUA estan protegits per drets d'autors. Alguns drets reservats.