• MuseReduce: A Generic Framework for Hierarchical Music Analysis

    Author(s):
    Petter Ericson, Yannis Rammos, Martin Rohrmeier
    Editor(s):
    Ailynn Ang, Jennifer Bain, David M. Weigl (see profile)
    Date:
    2023
    Group(s):
    Music Encoding Initiative
    Subject(s):
    Digital humanities, Music
    Item Type:
    Conference paper
    Conf. Title:
    Music Encoding Conference 2022
    Conf. Org.:
    Dalhousie University
    Conf. Loc.:
    Halifax, Nova Scotia, Canada
    Conf. Date:
    May 19-22, 2022
    Tag(s):
    computational music theory, graph representation, hierarchical analysis, Music analysis, music analysis corpora, Music encoding
    Permanent URL:
    https://doi.org/10.17613/mczq-3s12
    Abstract:
    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.
    Metadata:
    Status:
    Published
    Last Updated:
    3 months ago
    License:
    Attribution-NonCommercial-NoDerivatives
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