• Musicologists and Data Scientists Pull out all the Stops: Defining Renaissance Cadences Systematically

    Author(s):
    Richard Freedman, Alexander Morgan, Daniel Russo-Batterham
    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):
    Analysis, data science, Music encoding, musicology, Music Theory
    Permanent URL:
    https://doi.org/10.17613/6b2j-er77
    Abstract:
    Digital tools offer many ways to find musical patterns with machines. But the task of formulating digital-musical queries systematically, interpreting the results, and refining our methods to yield intelligent insights about musical practice is far more difficult. In this presentation, a team of musicologists and data scientists will share our experiences in developing CRIM Intervals, a Python-Pandas toolkit designed to support Citations: The Renaissance Imitation Mass, modeling human expertise in terms that can be used by computers to analyze encoded musical scores, and presenting the results of automated score-reading in forms that scholars can interrogate and refine. This presentation explains how we developed these tools, from understanding the constraints that define a given musical event, to the development of the tools needed to model those constraints, and in turn to the stages of refinement needed to eliminate false negatives and positives.
    Metadata:
    Status:
    Published
    Last Updated:
    6 months ago
    License:
    Attribution-NonCommercial-NoDerivatives

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