Learning to Read and Follow Music in Complete Score Sheet Images
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Abstract (arXiv)
This paper addresses the task of score following in sheet music given as unprocessed images. While existing work either relies on OMR software to obtain a computer-readable score representation, or crucially relies on prepared sheet image excerpts, we propose the first system that directly performs score following in full-page, completely unprocessed sheet images. Based on incoming audio and a given image of the score, our system directly predicts the most likely position within the page that matches the audio, outperforming current state-of-the-art image-based score followers in terms of alignment precision. We also compare our method to an OMR-based approach and empirically show that it can be a viable alternative to such a system.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1.
4 papers.
- 2022A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronizationcited by 5
- 2024Just Label the Repeats for In-The-Wild Audio-to-Score Alignmentcited by 2
- 2021Multi-modal Conditional Bounding Box Regression for Music Score Followingcited by 1
- 2022Proceedings of the 3rd International Workshop on Reading Music Systemscited by 0
- 2024Just Label the Repeats for In-The-Wild Audio-to-Score Alignment11 Nov 2024
- 2022Proceedings of the 3rd International Workshop on Reading Music Systems1 Dec 2022
- 2022A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization19 Apr 2022
- 2021Multi-modal Conditional Bounding Box Regression for Music Score Following10 May 2021