Alfaro-Contreras, María
Few-Shot Music Symbol Classification via Self-Supervised Learning and Nearest Neighbor
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
URI: http://hdl.handle.net/10045/138494
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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.
Keywords:Music Symbol Classification, Optical Music Recognition, Self-Supervised Learning, Few-shot Learning
International Workshop on Reading Music Systems
info:eu-repo/semantics/conferenceObject