skip to main content
10.1145/3625135.3625145acmotherconferencesArticle/Chapter ViewAbstractPublication PagesdlfmConference Proceedingsconference-collections
short-paper

MonodiKit: A data model and toolkit for medieval monophonic chant

Published: 10 November 2023 Publication History

Abstract

We present MonodiKit, a Python library for the analysis and processing of medieval chant documents. While MonodiKit was designed specifically for working with data in the monodi+ data format as edited by the Corpus Monodicum project, its comprehensive set of tools and classes offers a wide range of functionalities, such as parsing and processing of chant documents, exploring their hierarchical structure, managing metadata, generating musical notation, and extracting relevant information. The library thus enables researchers and scholars to conduct in-depth computational analyses of the chants, in particular by providing a basic interface to the more common MEI format. This paper introduces the key features of MonodiKit, presents its design and implementation, and presents a case study demonstrating its capabilities as an interface for corpus studies of medieval chant.

References

[1]
Manuel Anglada-Tort, Peter M.C. Harrison, Harin Lee, and Nori Jacoby. 2023. Large-Scale Iterated Singing Experiments Reveal Oral Transmission Mechanisms Underlying Music Evolution. Current Biology 33, 8 (2023), 1472–1486.e12. https://doi.org/10.1016/j.cub.2023.02.070
[2]
Carlos Cancino-Chacón, Silvan David Peter, Emmanouil Karystinaios, Francesco Foscarin, Maarten Grachten, and Gerhard Widmer. 2022. Partitura: A Python Package for Symbolic Music Processing. arxiv:2206.01071 [cs, eess] http://arxiv.org/abs/2206.01071
[3]
Nathaniel Condit-Schultz and Claire Arthur. 2019. humdrumR: A New Take on an Old Approach to Computational Musicology. In Proceedings of the 20th International Society for Music Information Retrieval Conference. ISMIR, Delft, The Netherlands, 715–722. https://doi.org/10.5281/zenodo.3527910
[4]
Bas Cornelissen, Willem Zuidema, and John Ashley Burgoyne. 2020. Mode Classification and Natural Units in Plainchant. In Proceedings of the 21st International Society for Music Information Retrieval Conference. ISMIR, Montreal, Canada, 869–875. https://doi.org/10.5281/zenodo.4245572
[5]
Bas Cornelissen, Willem Zuidema, and John Ashley Burgoyne. 2020. Studying Large Plainchant Corpora Using Chant21. In 7th International Conference on Digital Libraries for Musicology(DLfM 2020). Association for Computing Machinery, New York, NY, USA, 40–44. https://doi.org/10.1145/3424911.3425514
[6]
Michael Scott Cuthbert and Christopher Ariza. 2010. Music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data. In 11th International Society for Music Information Retrieval Conference (ISMIR 2010)2, J. Stephen Downie and Remco C. Veltkamp (Eds.). Inernational Society for Music Information Retrieval, Utrecht, 637–642.
[7]
Erin D. Foster and Ariel Deardorff. 2017. Open Science Framework (OSF). Journal of the Medical Library Association : JMLA 105, 2 (April 2017), 203–206. https://doi.org/10.5195/jmla.2017.88
[8]
Ichiro Fujinaga, Andrew Hankinson, and Julie E. Cumming. 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@JCDL 2014, September 12, 2014, Ben Fields and Kevin R. Page (Eds.). ACM, London, UK, 1–3. https://doi.org/10.1145/2660168.2660184
[9]
Maarten Grachten, Josep Lluís Arcos Rosell, and Ramón López de Mántaras. 2005. Melody Retrieval Using the Implication/Realization Model. In Proceedings of The6th International Conference on Music Information Retrieval. Queen Mary University of London, London, UK, 1–5. https://digital.csic.es/handle/10261/3222
[10]
Andrew Hankinson, Perry Roland, and Ichiro Fujinaga. 2011. The Music Encoding Initiative as a Document-Encoding Framework. In Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011, October 24-28, 2011, Anssi Klapuri and Colby Leider (Eds.). University of Miami, Miami, Florida, 293–298. http://ismir2011.ismir.net/papers/OS3-1.pdf
[11]
Johannes Hentschel and Martin Rohrmeier. 2023. ms3: A Parser for MuseScore Files, Serving as Data Factory for Annotated Music Corpora. Journal of Open Source Software 8, 88 (Aug. 2023), 5195. https://doi.org/10.21105/joss.05195
[12]
Elaine Hild and Thomas Weber. 2016. Developing Encoding and Software Solutions: For the Digital and Analogue Publication of Medieval Monophonic Music. In Music Encoding Conference Proceedings 2013 and 2014, Perry Roland and Johannes Kepper (Eds.). Bavarian State Library (BSB), Mainz Academy for Literature and Sciences, Mainz, Germany, 83–90.
[13]
David Huron. 1996. The Melodic Arch in Western Folksongs. Computing in Musicology 10 (1996), 3–23.
[14]
David Huron. 2002. Music Information Processing Using the Humdrum Toolkit: Concepts, Examples, and Lessons. Computer Music Journal 26, 2 (June 2002), 11–26. https://doi.org/10.1162/014892602760137158
[15]
David Brian Huron. 2006. Sweet Anticipation: Music and the Psychology of Expectation. MIT Press, Cambridge, MA.
[16]
David Lewis, David Weigl, and Kevin Page. 2019. Musicological Observations During Rehearsal and Performance: A Linked Data Digital Library for Annotations. In 6th International Conference on Digital Libraries for Musicology(DLfM ’19). Association for Computing Machinery, New York, NY, USA, 1–8. https://doi.org/10.1145/3358664.3358669
[17]
Franco Moretti. 2013. Distant Reading. Verso, London.
[18]
Fabian C. Moss and Markus Neuwirth. 2021. FAIR, Open, Linked: Introducing the Special Issue on Open Science in Musicology. Empirical Musicology Review 16, 1 (Dec. 2021), 1–4. https://doi.org/10.18061/emr.v16i1.8246
[19]
Stefan Münnich. 2021. FAIR for Whom? Commentary on Hofmann et al. (2021). Empirical Musicology Review 16, 1 (Dec. 2021), 151–153. https://doi.org/10.18061/emr.v16i1.8154
[20]
Eugene Narmour. 1990. The Analysis and Cognition of Basic Melodic Structures: The Implication-Realization Model. University of Chicago Press, Chicago.
[21]
Laurent Pugin. 2015. The Challenge of Data in Digital Musicology. Frontiers in Digital Humanities 2 (Aug. 2015), 1–3. https://doi.org/10.3389/fdigh.2015.00004
[22]
Laurent Pugin, Rodolfo Zitellini, and Perry Roland. 2014. Verovio: A Library for Engraving MEI Music Notation into SVG. In Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014), Hsin-Min Wang, Yi-Hsuan Yang, and Jin Ha Lee (Eds.). Academia Sinica, Taipei, Taiwan, 107–112. https://doi.org/10.5281/zenodo.1417589
[23]
E. Glenn Schellenberg. 1996. Expectancy in melody: tests of the implication-realization model. Cognition 58, 1 (Jan. 1996), 75–125. https://doi.org/10.1016/0010-0277(95)00665-6
[24]
David M. Weigl, Tim Crawford, Aggelos Gkiokas, Werner Goebl, Emilia Gómez, Nicolás F. Gutiérrez, Cynthia C. S. Liem, and Patricia Santos. 2021. FAIR Interconnection and Enrichment of Public-Domain Music Resources on the Web. Empirical Musicology Review 16, 1 (Dec. 2021), 16–33. https://doi.org/10.18061/emr.v16i1.7643
[25]
Oliver Weinreich. 2023. Zur digitalen Transformation in den Geisteswissenschaften. o-bib. Das offene Bibliotheksjournal / Herausgeber VDB 10, 1 (March 2023), 1–8. https://doi.org/10.5282/o-bib/5913
[26]
Tillman Weyde, Mathieu Barthet, Nicolas Gold, Samer Abdallah, Aquiles Alancar-Brayner, Mahendra Mahey, Adam Tovell, Stephen Cottrell, Jason Dykes, Emmanouil Benetos, Daniel Wolff, Dan Tidhar, Alexander Kachkaev, Mark Plumbley, and Simon Dixon. 2014. Big Data for Musicology. In Proceedings of the 1st International Workshop on Digital Libraries for Musicology - DLfM ’14. ACM Press, London, United Kingdom, 1–3. https://doi.org/10.1145/2660168.2660187
[27]
Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, Alasdair J.G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A.C ’t Hoen, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, and Barend Mons. 2016. The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific Data 3, 1 (March 2016), 160018. https://doi.org/10.1038/sdata.2016.18

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
DLfM '23: Proceedings of the 10th International Conference on Digital Libraries for Musicology
November 2023
139 pages
ISBN:9798400708336
DOI:10.1145/3625135
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 November 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Python
  2. corpus studies
  3. data model
  4. medieval chant
  5. music encoding

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

DLfM 2023

Acceptance Rates

Overall Acceptance Rate 27 of 48 submissions, 56%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 39
    Total Downloads
  • Downloads (Last 12 months)25
  • Downloads (Last 6 weeks)4
Reflects downloads up to 13 Jan 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media