Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3358664.3358667acmotherconferencesArticle/Chapter ViewAbstractPublication PagesdlfmConference Proceedingsconference-collections
short-paper

A notation-based query language for searching in symbolic music

Published: 09 November 2019 Publication History

Abstract

Existing systems for searching in symbolic music corpora generally suffer from either of two limitations: they are either limited in power because they accept only simple search patterns, or they are hard for musicologists and musicians to use because they require knowledge of programming and text processing tools. In this paper, we propose a new music query language that combines the best of both worlds: it is powerful enough to express a wide variety of complex search patterns, while at the same time being easy to use for musicologists and musicians because it is entirely based on standard music notation.
Our query system consists of several components. First, we define extensions of standard music notation that constitute the primitives of our musicological query language, such as pitch-only matching, rhythm-only matching, note grouping, and so on. Users may arbitrarily combine these primitives to create complex queries. Second, we define an XML-based encoding scheme that is an extension of Music Encoding Initiative in order to represent and store queries digitally. We then present an execution engine that runs XML-encoded queries against a corpus of music and produces search results. Finally, we describe a graphical interface that allows users to input queries and examine their results.
The main use case of our system is searching in corpora of monophonic music such as song melodies, folk tunes, musical themes, and solo instrumental music. In comparison with existing symbolic music search tools, our query language makes advanced searching more accessible to a wide audience of musicologists and musicians.

References

[1]
Anders Askenfelt. 1979. Automatic Notation of Played Music: The VISA Project. Fontes Artis Musicae 26, 2 (1979), 109–120.
[2]
Mathieu Bergeron and Darrell Conklin. 2008. Structured Polyphonic Patterns. In Proceedings of the 9th International Society for Music Information Retrieval Conference. Philadelphia, PA, USA, 69–74.
[3]
Michael S. Cuthbert and Christopher Ariza. 2010. Music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data. In Proceedings of the 11th International Society for Music Information Retrieval Conference. Utrecht, Netherlands, 637–642. https://hdl.handle.net/1721.1/84963
[4]
David Garfinkle, Peter Schubert, Claire Arthur, Julie Cumming, and Ichiro Fujinaga. 2017. PatternFinder: Content-Based Music Retrieval with Music21. In Proceedings of the 4th International Workshop on Digital Libraries for Musicology. Shanghai, China, 5–8. https://doi.org/10.1145/3144749.3144751
[5]
Mark Gotham, Peter Jonas, Bruno Bower, William Bosworth, Daniel Rootham, and Leigh VanHandel. 2018. Scores of Scores: An OpenScore Project to Encode and Share Sheet Music. In Proceedings of the 5th International Conference on Digital Libraries for Musicology. Paris, France, 87–95. https://doi.org/10.1145/3273024.3273026
[6]
Jan Hajič jr., Marta Kolárová, Alexander Pacha, and Jorge Calvo-Zaragoza. 2018. How Current Optical Music Recognition Systems Are Becoming Useful for Digital Libraries. In Proceedings of the 5th International Conference on Digital Libraries for Musicology. Paris, France, 57–61. https://doi.org/10.1145/3273024.3273034
[7]
David Huron. 2002. Music Information Processing Using the Humdrum Toolkit: Concepts, Examples, and Lessons. Computer Music Journal 26, 2 (2002), 11–26. https://doi.org/10.1162/014892602760137158
[8]
Steven Jan. 2004. Meme Hunting with the Humdrum Toolkit: Principles, Problems, and Prospects. Computer Music Journal 28, 4 (2004), 68–84. https://doi.org/10.1162/0148926042728403
[9]
Andreas Kornstädt. 1998. Themefinder: A Web-Based Melodic Search Tool. Computing in Musicology 11 (1998), 231–236.
[10]
Casey A. Mullin. 2010. Review of International Music Score Library Project/Petrucci Music Library. Notes 67, 2 (2010), 376–381.
[11]
Stephen Dowland Page. 1988. Computer Tools for Music Information Retrieval. Ph.D. Dissertation. University of Oxford, UK.
[12]
Lutz Prechelt and Rainer Typke. 2001. An Interface for Melody Input. ACM Transactions on Computer-Human Interaction 8, 2(2001), 133–149. https://doi.org/10.1145/376929.376978
[13]
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. Taipei, Taiwan, 107–112.
[14]
Perry Roland. 2002. The Music Encoding Initiative (MEI). In Proceedings of the First International Conference on Musical Applications Using XML. Milan, Italy, 55–59.
[15]
Helmut Schaffrath. 1995. The Essen Folksong Collection in the Humdrum Kern Format. Center for Computer-Assisted Research in the Humanities, Menlo Park, CA, USA.
[16]
Ken Thompson. 1968. Programming Techniques: Regular Expression Search Algorithm. Commun. ACM 11, 6 (1968), 419–422. https://doi.org/10.1145/363347.363387
[17]
Rainer Typke, Frans Wiering, and Remco C. Veltkamp. 2005. A Survey of Music Information Retrieval Systems. In Proceedings of the 6th International Society for Music Information Retrieval Conference. London, UK, 153–160.
[18]
Vladimir Viro. 2011. Peachnote: Music Score Search and Analysis Platform. In Proceedings of the 12th International Society for Music Information Retrieval Conference. Miami, FL, USA, 359–362.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
DLfM '19: Proceedings of the 6th International Conference on Digital Libraries for Musicology
November 2019
88 pages
ISBN:9781450372398
DOI:10.1145/3358664
  • Conference Chair:
  • David Rizo
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: 09 November 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Humdrum
  2. Music Encoding Initiative
  3. computational musicology
  4. music searching
  5. query language
  6. regular expressions
  7. symbolic music

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

DLfM '19

Acceptance Rates

Overall Acceptance Rate 27 of 48 submissions, 56%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 108
    Total Downloads
  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 30 Aug 2024

Other Metrics

Citations

View Options

Get Access

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