Summary of Optical Music Recognition in Manuscripts From the Ricordi Archive, by Federico Simonetta et al.
Optical Music Recognition in Manuscripts from the Ricordi Archive
by Federico Simonetta, Rishav Mondal, Luca Andrea Ludovico, Stavros Ntalampiras
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to music analysis is proposed, leveraging the Ricordi archive’s digitized musical manuscripts from renowned opera composers. By extracting samples of various musical elements, including notes, staves, and annotations, a subset was labeled by multiple individuals into classes. Neural network-based classifiers were trained to differentiate between these elements, with the goal of automatically categorizing the remaining unannotated dataset. This study evaluates the reliability of these classifiers for potential use in music information retrieval applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has turned historic musical manuscripts into digital files. They then used computers to pick out specific parts like notes and staff lines. People worked together to label these pieces, grouping them into categories. Next, they trained special computer models called neural networks to identify different types of music elements. The goal is to use these models to automatically sort through the rest of the unorganized data. The team wants to make sure their approach works well before applying it to all the remaining files. |
Keywords
» Artificial intelligence » Neural network
