An Online Tool for Semi-Automatically Annotating Music Scores for Optical Music Recognition
Authors: 
Stanisław Graczyk, Zuzanna Piniarska, Mateusz Kałamoniak, Tomasz Łukaszewski, Ewa ŁukasikAuthors Info & Claims
Stanisław Graczyk, Zuzanna Piniarska, Mateusz Kałamoniak, Tomasz Łukaszewski, Ewa ŁukasikAuthors Info & ClaimsPages 73 - 77
Abstract
The paper describes an online tool, OMRAT, for semi-automatic annotation of music scores for Optical Music Recognition (OMR) systems. OMRAT uses deep neural networks, machine learning, and music notation ontologies at different stages to respectively detect musical objects, establish relationships between them, and convert them into a machine-readable format MEI. A human editor verifies the output of the recognition stage to correct potential errors and remove incorrect labels as needed. The tool can create training/testing datasets for OMR systems and may be used for notation editors or audio synthesizers.
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- An Online Tool for Semi-Automatically Annotating Music Scores for Optical Music Recognition
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Published: 27 June 2024
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- Short-paper
- Research
- Refereed limited
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- Smart Growth Operational Programme 2014-2020 in the area ?Development of modern research infrastructure of the science sector?
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DLfM 2024
DLfM 2024: 11th International Conference on Digital Libraries for Musicology
June 27, 2024
Stellenbosch, South Africa
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Overall Acceptance Rate 27 of 48 submissions, 56%
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