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Data quality assessment in digital score libraries

The GioQoso Project

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Abstract

Sheet music scores have been the traditional way to preserve and disseminate western classical music works for centuries. Nowadays, their content can be encoded in digital formats that yield a very detailed representation of music content expressed in the language of music notation. These digital scores constitute, therefore, an invaluable asset for digital library services such as search, analysis, clustering, recommendations, and synchronization with audio files. Digital scores, like any other published data, may suffer from quality problems. For instance, they can contain incomplete or inaccurate elements. As a “dirty” dataset may be an irrelevant input for some use cases, users need to be able to estimate the quality level of the data they are about to use. This article presents the data quality management framework for digital score libraries (DSL) designed by the GioQoso multi-disciplinary project. It relies on a content model that identifies several information levels that are unfortunately blurred out in digital score encodings. This content model then serves as a foundation to organize the categories of quality issues that can occur in a music score, leading to a quality model. The quality model also positions each issue with respect to potential usage contexts, allowing attachment of a consistent set of indicators that together measure how a given score is fit to a specific usage. We finally report an implementation of these conceptual foundations in an online DSL.

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Notes

  1. The complete digital score is available at http://neuma.huma-num.fr/home/opus/composers:cherubini:Requiem_Cherubini_Dies_Ireae/.

  2. The partners of the GioQoso project were the BnF—Bibliothèque nationale de France (Paris, France), the CEDRIC laboratory of the CNAM (Paris, France), the CESR—Centre d’Études Supérieures de la Renaissance (Tours, France), the iReMus—Institut de recherche en Musicologie (Paris, France) and the IRISA of Univ. Rennes (Lannion, France).

  3. The metaphor also holds for the rendering step, carried out in the case of HTML by a web browser that adjusts the textual content and CSS rules to the displaying window.

  4. Of course, one can discuss this assignment according to the context as it only reflects a general trend of such users’ visions according to their roles.

References

  1. Acosta, M., Zaveri, A., Simperl, E., Kontokostas, D., Auer, S., Lehmann, J.: Crowdsourcing linked data quality assessment. In: Proceedings of the International Semantic Web Conference (ISWC’13), pp. 260–276 (2013)

  2. Basili, V.R., Caldiera, G., Rombach, H.D.: Encyclopedia of Software Engineering, chap. Wiley, The Goal Question Metric Approach (1994)

  3. Batini, C., Scannapieco, M.: Data Quality: Concepts. Springer, Methodologies and Techniques. Data-Centric Systems and Applications (2016)

  4. Besson, V., Fiala, D., Rigaux, P., Thion, V.: Gioqoso, an Online Quality Evaluation Tool for MEI Scores. In: Music Encoding Conference (MEC’18) (2018). https://hal.archives-ouvertes.fr/hal-01708859. Poster

  5. Besson, V., Gurrieri, M., Rigaux, P., Tacaille, A., Thion, V.: A methodology for quality assessment in collaborative score libraries. In: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR’16) (2016). https://hal.inria.fr/hal-01316014

  6. BnF’s digital library Gallica. http://gallica.bnf.fr

  7. Cambouropoulos, E.: Voice and stream: perceptual and computational modeling of voice separation. Music Percep.: An Interdiscip. J. 26(1), 75–94 (2008)

    Article  Google Scholar 

  8. Cherfi, S., Guillotel, C., Hamdi, F., Rigaux, P., Travers, N.: Ontology-based annotation of music scores. In: Intl. Conf. on Knowledge Capture (K-CAP’17) (2017). Austin, Texas, Dec. 4-6 2017

  9. Cherfi, S., Hamdi, F., Rigaux, P., Thion, V., Travers, N.: Formalizing quality rules on music notation an ontology-based approach. In: Intl. Conf. on Technologies for Music Notation and Representation (TENOR’17) (2017)

  10. Cuthbert, M.S., Ariza, C.: music21: A toolkit for computer-aided musicology and symbolic music data. In: Proceedings of the International Society for Music Information Retrieval (ISMIR’10) (2010)

  11. Eke, C., Norman, A., Shuib, L., Nweke, H.: A survey of user profiling: State-of-the-art, challenges and solutions. IEEE Access pp. 1–1 (2019). DOIurlhttps://doi.org/10.1109/ACCESS.2019.2944243

  12. Fiala, D., Rigaux, P., Tacaille, A., Thion, V., the members of The Gioqoso project: Data Quality Rules for Digital Score Libraries. Research report, IRISA, Université de Rennes (2018). https://hal.inria.fr/hal-01734821

  13. Foscarin, F., Fiala, D., Jacquemard, F., Rigaux, P., Thion, V.: Gioqoso, an online quality assessment tool for music notation. In: Proceedings of the International Conference on Technologies for Music Notation and Representation (TENOR’18) (2018). https://hal.inria.fr/hal-01895171. Poster

  14. Gil, Y., Ratnakar, V.: Trusting information sources one citizen at a time. In: Proceedings of the International Semantic Web Conference (ISWC’02), pp. 162–176 (2002)

  15. Gioqoso project web site (consulted in 2020)

  16. Global Chant Database. http://www.globalchant.org/

  17. Good, M.: The Virtual Score: Representation, Retrieval, Restoration, chap. ”MusicXML for Notation and Analysis”, pp. 113–124. W. B. Hewlett and E. Selfridge-Field, MIT Press (2001)

  18. Gould, E.: Behind Bars. Faber Music (2011)

  19. International Music Score Library Project or Petrucci Music Library (IMSLP). https://imslp.org

  20. The lost voices project. http://www.digitalduchemin.org

  21. Music Encoding Initiative. http://www.music-encoding.org (2015). Accessed April 2019

  22. Music Notation Community Group. https://www.w3.org/community/music-notation/ (2018). Accessed April 2019

  23. NEUMA. http://neuma.huma-num.fr

  24. The Open Score Project (consulted in 2020). https://www.open-score.fr

  25. Pugin, L., Zitellini, R., Roland, P.: Verovio: A library for engraving MEI music notation into SVG. In: Proceedings of the International Society for Music Information Retrieval (ISMIR’14), pp. 107–112 (2014). http://verovio.org

  26. Redman, T.C.: Data Quality for the Information Age. Artech House Inc. (1996)

  27. Rigaux, P., Abrouk, L., Audéon, H., Cullot, N., Davy-Rigaux, C., Faget, Z., Gavinet, E., Gross-Amblard, D., Tacaille, A., Thion, V.: The design and implementation of NEUMA, a collaborative digital score library—requirements, architecture, and models. Intl. Journal On Digital Libraries (JODL) pp. 1–24 (2012). https://hal.archives-ouvertes.fr/hal-01126064

  28. Riley, J., Mayer, C.A.: Ask a librarian: the role of librarians in the music information retrieval. In: Proceeding of International Conference on Music Information Retrieval (ISMIR) (2006)

  29. Rolland, P.: The music encoding initiative (MEI). In: Proceedings of the International Conference on Musical Applications Using XML, pp. 55–59 (2002)

  30. Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment for linked data: a survey. Semant. Web 7(1), 63–93 (2016). https://doi.org/10.3233/SW-150175

    Article  Google Scholar 

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Acknowledgements

This work has been partly funded by the French National Center for Scientific Research (CNRS) under the défi Mastodons GioQoso.

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Correspondence to Virginie Thion.

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Foscarin, F., Rigaux, P. & Thion, V. Data quality assessment in digital score libraries. Int J Digit Libr 22, 159–173 (2021). https://doi.org/10.1007/s00799-021-00299-7

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  • DOI: https://doi.org/10.1007/s00799-021-00299-7

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