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Musereduce: a generic framework for hierarchical music analysis
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-8722-5661
École Polytechnique, Fédérale de Lausanne, Switzerland.
École Polytechnique, Fédérale de Lausanne, Switzerland.
2023 (English)In: Music Encoding Conference 2022 Proceedings / [ed] Ailynn Ang; Jennifer Bain; David M. Weigl, Music Encoding Initiative , 2023, p. 40-51Conference paper, Published paper (Refereed)
Abstract [en]

In comparison to computational linguistics, with its abundance of natural-language datasets, corpora of music analyses are rather fewer and generally smaller. This is partly due to difficulties inherent to the encoding of music analyses, whose multimodal representations—typically a combination of music notation, graphic notation, and natural language—are designed for communication between human musician-analysts, not for automated large-scale data analysis. Analyses based on hierarchical models of tonal structure, such as Heinrich Schenker’s, present additional notational and encoding challenges, since they establish relations between non- adjacent tones, and typically interpret successions of tones as expressions of abstract chordal sonorities, which may not be literally present in the music score. Building on a published XML format by Rizo and Marsden (2019), which stores analyses alongside symbolically encoded scores, this paper presents a generic graph model for reasoning about music analyses, as well as a graphical web application for creating and encoding music analyses in the aforementioned XML format. Several examples are given showing how various techniques of music analysis, primarily but not necessarily hierarchical, might be unambiguously represented through this model.

Place, publisher, year, edition, pages
Music Encoding Initiative , 2023. p. 40-51
Keywords [en]
computational music theory, graph representation, hierarchical analysis, Music analysis, music analysis corpora, Music encoding
National Category
Musicology
Research subject
Musicology; Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-214920DOI: 10.17613/mczq-3s12OAI: oai:DiVA.org:umu-214920DiVA, id: diva2:1802260
Conference
Music Encoding Conference 2022, Halifax, Canada, May 19-22, 2022
Funder
EU, Horizon 2020, 760081 – PMSBAvailable from: 2023-10-04 Created: 2023-10-04 Last updated: 2023-10-05Bibliographically approved

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Ericson, Petter

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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  • en-US
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Output format
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  • asciidoc
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