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Content-Based Search Tools for Large Sets of Print and Manuscript Music Cover

Content-Based Search Tools for Large Sets of Print and Manuscript Music

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Open Access
|Jun 2026

References

  1. Antila, C., and Cumming, J. (2014). The VIS framework: Analyzing counterpoint in large datasets. In Proceedings of the 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan (pp. 7176). International Society for Music Information Retrieval.
  2. Brook, B. S. (1965). The simplified “Plaine and Easie Code System” for notating music: A proposal for international adoption. Fontes Artis Musicae, 12(2/3), 156160.
  3. Calvo‑Zaragoza, J., Hajič, J., and Pacha, A. (2020). Understanding optical music recognition. ACM Computing Surveys, 53(4), Article 77. 10.1145/3397499.
  4. Crawford, T., Lewis, D., and Porter, A. (2023). Exploring early vocal music and its lute arrangements: Using F‑Tempo as a musicological tool. In Proceedings of the 10th International Conference on Digital Libraries for Musicology, Milan Italy (pp. 7781). ACM.
  5. Downie, S. (1999). Evaluating a Simple Approach to Music Information Retrieval: Conceiving Melodic N‑Grams as Text. Faculty of Graduate Studies.
  6. Fujinaga, I., Hankinson, A., and Cumming, J. E. (2014). Introduction to SIMSSA (Single Interface for Music Score Searching and Analysis). In Proceedings of the 1st International Workshop on Digital Libraries for Musicology DLfM ’14, New York, NY, USA (pp. 13). Association for Computing Machinery.
  7. Fujinaga, I., and Vigliensoni, G. (2019). The art of teaching computers: The SIMSSA optical music recognition workflow system. In 2019 27th European Signal Processing Conference (EUSIPCO), A Coruña, Spain (pp. 15). European Association for Signal Processing.
  8. Hajič, J., Jr., Žabička, P., Rychtář, J., Mayer, J., Dvořáková, M., Jebavý, F., Vlková, M., and Pecina, P. (2023). The OmniOMR project. In J. Calvo‑Zaragoza, A. Pacha, and E. Shatri (Eds.), Proceedings of the 5th International Workshop on Reading Music Systems, Milan, Italy (pp. 1214). arXiv.
  9. Hankinson, A., Burgoyne, J. A., Vigliensoni, G., Porter, A., Thompson, J., Liu, W., Chiu, R., and Fujinaga, I. (2012). Digital document image retrieval using optical music recognition. In Proceedings of the 13th International Society for Music Information Retrieval Conference, Porto, Portugal. International Society for Music Information Retrieval.
  10. Helsen, K., Bain, J., Fujinaga, I., Hankinson, A., and Lacoste, D. (2014). Optical music recognition and manuscript chant sources. Early Music, 42(4), 555558. 10.1093/em/cau092.
  11. Lacoste, D. (2022). The Cantus Database and Cantus Index network. In D. Shanahan, J. A. Burgoyne, and I. Quinn (Eds.), The Oxford Handbook of Music and Corpus Studies (Online ed.). Oxford University Press.
  12. Padilla, V., McLean, A., Marsden, A., and Ng, K. (2015). Improving optical music recognition by combining outputs from multiple sources. In M. Müller and F. Wiering (Eds.), Proceedings of the 16th International Society for Music Information Retrieval Conference, Málaga, Spain (pp. 517523). International Society for Music Information Retrieval.
  13. Pulimootil Achankunju, S. (2018). Music search engine from noisy OMR data. In J. Calvo‑Zaragoza, A. Pacha, and J. Hajič Jr. (Eds.), Proceedings of the 1st International Workshop on Reading Music Systems, Paris, France (pp. 2324). arXiv.
  14. Rizo, D., Calvo‑Zaragoza, J., García‑Iasci, P., and Delgádo‑Sanchez, T. (2024). Lessons learned from a project to encode mensural music on a large scale with optical music recognition. In Proceedings of the 25th International Society for Music Information Retrieval Conference, San Francisco, San Francisco, CA, USA (pp. 225231). International Society for Music Information Retrieval.
  15. Rizo, D., Calvo‑Zaragoza, J., and Iñesta, J. M. (2018). MuRET: A music recognition, encoding, and transcription tool. In Proceedings of the 5th International Conference on Digital Libraries for Musicology, DLfM ’18, New York, NY, USA (pp. 5256). Association for Computing Machinery.
DOI: https://doi.org/10.5334/tismir.293 | Journal eISSN: 2514-3298
Language: English
Page range: 210 - 219
Submitted on: Jun 23, 2025
Accepted on: Mar 25, 2026
Published on: Jun 4, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Jürgen Diet, Janosch Umbreit, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.