Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases

M.E. Rodríguez López, Anja Volk

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Abstract

    Gestalt-based segmentation models constitute the current
    state of the art in automatic segmentation of melodies. These models
    commonly assume that segment boundary perception is mainly triggered by local discontinuities, i.e. by abrupt changes in pitch and/or duration between neighbouring notes. This paper presents a statistical study of a large corpus of boundary-annotated vocal melodies to test this assumption. The study focuses on analysing the statistical behaviour of pitch and duration in the neighbourhood of annotated phrase boundaries. Our analysis shows duration discontinuities to be statistically regular and homogeneous, and contrarily pitch discontinuities to be irregular and heterogeneous. We conclude that pitch discontinuities, when modelled as a local and idiom-independent phenomenon, ] can only serve as a weak predictor of segment boundary perception in vocal melodies.
    Original languageEnglish
    Title of host publicationSound, Music, and Motion
    Subtitle of host publicationPost Proceedings of the 10th international symposium, CMMR 2013
    Place of PublicationCham
    PublisherSpringer
    Pages548-557
    Number of pages10
    ISBN (Electronic)978-3-319-12976-1
    ISBN (Print)978-3-319-12975-4
    DOIs
    Publication statusPublished - 2014

    Publication series

    NameLecture notes in computer science
    PublisherSpringer
    Volume8495

    Fingerprint

    Dive into the research topics of 'Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases'. Together they form a unique fingerprint.

    Cite this