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-2 | English |
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Titel | Music in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers |
Redacteuren | Mitsuko Aramaki, Richard Kronland-Martinet, Sølvi Ystad, Keiji Hirata, Tetsuro Kitahara |
Uitgeverij | Springer Science and Business Media Deutschland GmbH |
Pagina's | 282-300 |
Aantal pagina's | 19 |
ISBN van geprinte versie | 9783031353819 |
DOI's | |
Status | Published - 2023 |
Evenement | 15th International Symposium on Computer Music Multidisciplinary Research, CMMR 2021 - Virtual Online Duur: 15 nov 2021 → 19 nov 2021 |
Publicatie series
Naam | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13770 LNCS |
ISSN van geprinte versie | 0302-9743 |
ISSN van elektronische versie | 1611-3349 |
Conference
Conference | 15th International Symposium on Computer Music Multidisciplinary Research, CMMR 2021 |
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Stad | Virtual Online |
Periode | 15/11/21 → 19/11/21 |
Bibliografische nota
Publisher Copyright:© 2023, Springer Nature Switzerland AG.