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Musical Structure Analysis and Generation Through Abstraction Trees

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Music in the AI Era (CMMR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13770 ))

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Abstract

“Structure” is a somewhat elusive concept in music, despite being of extreme importance in a variety of applications. Being inherently a hidden feature, it is not always explicitly considered in algorithms and representations of music. We propose a hierarchical approach to the study of musical structures, that builds upon tree representations of music like Schenkerian analysis, and adds additional layers of abstraction introducing pairwise comparisons between these trees. Finally, these representations can be joined into probabilistic representations of a music corpus. The probability distributions contained in these representation allow us to use concepts from Information Theory to show how the structures we introduce can be applied to musicological, music information retrieval applications and structure-aware music generation.

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Acknowledgments

FC received funding from the University of Padova, from Fondazione Ing. Aldo Gini, and from the Department of Information Engineering of the University of Padova. NH, ST and GW received funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme.

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Authors and Affiliations

  1. University of Padova, Padua, Italy

    Filippo Carnovalini & Antonio Rodà

  2. Vrije Universiteit Brussel, Brussels, Belgium

    Nicholas Harley, Steven T. Homer & Geraint A. Wiggins

  3. Queen Mary University of London, London, UK

    Geraint A. Wiggins

Authors
  1. Filippo Carnovalini
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  2. Nicholas Harley
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  3. Steven T. Homer
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  4. Antonio Rodà
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  5. Geraint A. Wiggins
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Corresponding author

Correspondence to Filippo Carnovalini .

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Editors and Affiliations

  1. Aix-Marseille Univ, Marseille Cedex 09, France

    Mitsuko Aramaki

  2. Future University Hakodate, Hakodate, Hokkaido, Japan

    Keiji Hirata

  3. Nihon University, Tokyo, Japan

    Tetsuro Kitahara

  4. Aix-Marseille Univ, Marseille Cedex 09, France

    Richard Kronland-Martinet

  5. Aix-Marseille Univ, Marseille Cedex 09, France

    Sølvi Ystad

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Carnovalini, F., Harley, N., Homer, S.T., Rodà, A., Wiggins, G.A. (2023). Musical Structure Analysis and Generation Through Abstraction Trees. In: Aramaki, M., Hirata, K., Kitahara, T., Kronland-Martinet, R., Ystad, S. (eds) Music in the AI Era. CMMR 2021. Lecture Notes in Computer Science, vol 13770 . Springer, Cham. https://doi.org/10.1007/978-3-031-35382-6_22

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  • DOI: https://doi.org/10.1007/978-3-031-35382-6_22

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