Musical Structure Analysis and Generation Through Abstraction Trees

Onderzoeksoutput: Conference paper

Samenvatting

“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.

Originele taal-2English
TitelMusic in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers
RedacteurenMitsuko Aramaki, Richard Kronland-Martinet, Sølvi Ystad, Keiji Hirata, Tetsuro Kitahara
UitgeverijSpringer Science and Business Media Deutschland GmbH
Pagina's282-300
Aantal pagina's19
ISBN van geprinte versie9783031353819
DOI's
StatusPublished - 2023
Evenement15th International Symposium on Computer Music Multidisciplinary Research, CMMR 2021 - Virtual Online
Duur: 15 nov 202119 nov 2021

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13770 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Conference

Conference15th International Symposium on Computer Music Multidisciplinary Research, CMMR 2021
StadVirtual Online
Periode15/11/2119/11/21

Bibliografische nota

Publisher Copyright:
© 2023, Springer Nature Switzerland AG.

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