arXiv:2607.05769v1 Announce Type: cross Abstract: We propose a novel pipeline, Legato 2, for extracting symbolic notation and semantic knowledge from images of sheet music. Legato 2 features the first large-scale neural model for optical music recognition (OMR) to operate sequentially on a system-by-system basis, following the horizontal lines of notation as they are read on the page, rather than treating the page as an undifferentiated image, enabling better scaling to arbitrarily long inputs. It is also the first OMR model capable of generating symbolic transcriptions that include embedded t
Advances in multimodal AI and larger computational resources are enabling more sophisticated models for niche applications like optical music recognition, moving beyond general image processing.
This breakthrough represents a significant step in AI's ability to interpret complex, structured visual information, opening new avenues for digital archiving, musicology, and creative tool development.
The ability to sequentially process sheet music and generate symbolic transcriptions with embedded 't' (likely timing/tempo) information fundamentally improves the accuracy and utility of OMR systems.
- · AI researchers (multimodal)
- · Music archivists and libraries
- · Music technology companies
- · Composers and musicians
- · Manual music transcribers
- · Legacy OMR software providers
More accurate and efficient digital indexing and search of sheet music collections will become possible.
AI could develop new tools for musical analysis, composition assistance, and automated performance generation.
The principles behind sequential processing of structured visual data could be adapted to other complex domains beyond music.
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Read at arXiv cs.AI