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Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases

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Sound, Music, and Motion (CMMR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8905))

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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.

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Notes

  1. 1.

    In the project we pay special attention to the role that segments play in assessing the variation and similarity between melodies.

  2. 2.

    Listening studies have commonly focused on testing segmentation theories using small datasets annotated by a high number of human listeners.

  3. 3.

    We take a context size of \([-2,2]\) as it commonly constitutes the upper limit for context sizes in comparative studies of melodic segmentation models [18, 19, 24, 26] (beyond this value the performance of Gestalt based models either drops or does not seems to result in significant improvements).

  4. 4.

    To avoid a bias of subset size and phrase-group size on the statistical significance testing, we created equal size groups of both subsets and of all size-groups using random sampling.

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Acknowledgments

We thank F. Wiering and the anonymous reviewers for the useful comments on earlier drafts of this document. M.E. Rodríguez-López and A. Volk are supported by the Netherlands Organization for Scientific Research, NWO-VIDI grant 276-35-001.

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Authors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands

    Marcelo Rodríguez-López & Anja Volk

Authors
  1. Marcelo Rodríguez-López
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  2. Anja Volk
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Correspondence to Marcelo Rodríguez-López .

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Editors and Affiliations

  1. CNRS - LMA, Marseille, France

    Mitsuko Aramaki

  2. Toulon-Var University and CNRS - LMA, Marseille, France

    Olivier Derrien

  3. CNRS - LMA, Marseille, France

    Richard Kronland-Martinet

  4. CNRS - LMA, Marseille, France

    Sølvi Ystad

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Rodríguez-López, M., Volk, A. (2014). Symbolic Segmentation: A Corpus-Based Analysis of Melodic Phrases. In: Aramaki, M., Derrien, O., Kronland-Martinet, R., Ystad, S. (eds) Sound, Music, and Motion. CMMR 2013. Lecture Notes in Computer Science(), vol 8905. Springer, Cham. https://doi.org/10.1007/978-3-319-12976-1_33

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  • DOI: https://doi.org/10.1007/978-3-319-12976-1_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12975-4

  • Online ISBN: 978-3-319-12976-1

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