Musical Structure Analysis and Generation Through Abstraction Trees

Filippo Carnovalini; Nicholas Harley; Steven T. Homer; Antonio Rodà; Geraint A. Wiggins

doi:10.1007/978-3-031-35382-6_22

Musical Structure Analysis and Generation Through Abstraction Trees

Filippo Carnovalini, Nicholas Harley, Steven T. Homer, Antonio Rodà, Geraint A. Wiggins

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

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.

Original language	English
Title of host publication	Music in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers
Editors	Mitsuko Aramaki, Richard Kronland-Martinet, Sølvi Ystad, Keiji Hirata, Tetsuro Kitahara
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	282-300
Number of pages	19
ISBN (Print)	9783031353819
DOIs	https://doi.org/10.1007/978-3-031-35382-6_22
Publication status	Published - 2023
Event	15th International Symposium on Computer Music Multidisciplinary Research, CMMR 2021 - Virtual Online Duration: 15 Nov 2021 → 19 Nov 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13770 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Symposium on Computer Music Multidisciplinary Research, CMMR 2021
City	Virtual Online
Period	15/11/21 → 19/11/21

Bibliographical note

Funding Information:
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.

Funding Information:
Acknowledgments. FC received funding from the University of Padova, from Fon-dazione 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.

Publisher Copyright:
© 2023, Springer Nature Switzerland AG.

Keywords

music generation
music representations
Schenkerian analysis
structure

Access to Document

10.1007/978-3-031-35382-6_22

Cite this

Carnovalini, F., Harley, N., Homer, S. T., Rodà, A., & Wiggins, G. A. (2023). Musical Structure Analysis and Generation Through Abstraction Trees. In M. Aramaki, R. Kronland-Martinet, S. Ystad, K. Hirata, & T. Kitahara (Eds.), Music in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers (pp. 282-300). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13770 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-35382-6_22

Carnovalini, Filippo ; Harley, Nicholas ; Homer, Steven T. ; Rodà, Antonio ; Wiggins, Geraint A. / Musical Structure Analysis and Generation Through Abstraction Trees. Music in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers. editor / Mitsuko Aramaki ; Richard Kronland-Martinet ; Sølvi Ystad ; Keiji Hirata ; Tetsuro Kitahara. Springer Science and Business Media Deutschland GmbH, 2023. pp. 282-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{750929b563f54628a23bd7c449775eba,

title = "Musical Structure Analysis and Generation Through Abstraction Trees",

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.",

keywords = "music generation, music representations, Schenkerian analysis, structure",

author = "Filippo Carnovalini and Nicholas Harley and Homer, {Steven T.} and Antonio Rod{\`a} and Wiggins, {Geraint A.}",

note = "Funding Information: 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 Artifici{\"e}le Intelligentie (AI) Vlaanderen” programme. Funding Information: Acknowledgments. FC received funding from the University of Padova, from Fon-dazione 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 Artifici{\"e}le Intelligentie (AI) Vlaanderen” programme. Publisher Copyright: {\textcopyright} 2023, Springer Nature Switzerland AG.; 15th International Symposium on Computer Music Multidisciplinary Research, CMMR 2021 ; Conference date: 15-11-2021 Through 19-11-2021",

year = "2023",

doi = "10.1007/978-3-031-35382-6_22",

language = "English",

isbn = "9783031353819",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "282--300",

editor = "Mitsuko Aramaki and Richard Kronland-Martinet and S{\o}lvi Ystad and Keiji Hirata and Tetsuro Kitahara",

booktitle = "Music in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers",

address = "Germany",

}

Carnovalini, F , Harley, N , Homer, ST, Rodà, A & Wiggins, GA 2023, Musical Structure Analysis and Generation Through Abstraction Trees. in M Aramaki, R Kronland-Martinet, S Ystad, K Hirata & T Kitahara (eds), Music in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13770 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 282-300, 15th International Symposium on Computer Music Multidisciplinary Research, CMMR 2021, Virtual Online, 15/11/21. https://doi.org/10.1007/978-3-031-35382-6_22

Musical Structure Analysis and Generation Through Abstraction Trees. / Carnovalini, Filippo ; Harley, Nicholas ; Homer, Steven T.; Rodà, Antonio; Wiggins, Geraint A.

Music in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers. ed. / Mitsuko Aramaki; Richard Kronland-Martinet; Sølvi Ystad; Keiji Hirata; Tetsuro Kitahara. Springer Science and Business Media Deutschland GmbH, 2023. p. 282-300 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13770 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

TY - GEN

T1 - Musical Structure Analysis and Generation Through Abstraction Trees

AU - Carnovalini, Filippo

AU - Harley, Nicholas

AU - Homer, Steven T.

AU - Rodà, Antonio

AU - Wiggins, Geraint A.

N1 - Funding Information: 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. Funding Information: Acknowledgments. FC received funding from the University of Padova, from Fon-dazione 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. Publisher Copyright: © 2023, Springer Nature Switzerland AG.

PY - 2023

Y1 - 2023

N2 - “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.

AB - “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.

KW - music generation

KW - music representations

KW - Schenkerian analysis

KW - structure

UR - http://www.scopus.com/inward/record.url?scp=85165000615&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-35382-6_22

DO - 10.1007/978-3-031-35382-6_22

M3 - Conference paper

AN - SCOPUS:85165000615

SN - 9783031353819

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 282

EP - 300

BT - Music in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers

A2 - Aramaki, Mitsuko

A2 - Kronland-Martinet, Richard

A2 - Ystad, Sølvi

A2 - Hirata, Keiji

A2 - Kitahara, Tetsuro

PB - Springer Science and Business Media Deutschland GmbH

T2 - 15th International Symposium on Computer Music Multidisciplinary Research, CMMR 2021

Y2 - 15 November 2021 through 19 November 2021

ER -

Carnovalini F , Harley N , Homer ST, Rodà A, Wiggins GA. Musical Structure Analysis and Generation Through Abstraction Trees. In Aramaki M, Kronland-Martinet R, Ystad S, Hirata K, Kitahara T, editors, Music in the AI Era - 15th International Symposium, CMMR 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. p. 282-300. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-031-35382-6_22

Musical Structure Analysis and Generation Through Abstraction Trees

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Cite this