Network representations of drum sequences for classification and generation

Complex networks have emerged as a powerful framework for understanding and analyzing musical compositions, revealing underlying structures and dynamics that may not be immediately apparent. This article explores the application of complex network representations to the study of symbolic drum sequen...

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Detalles Bibliográficos
Autores: Gómez-Marín, Daniel, Jordà Puig, Sergi, Herrera, Perfecto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/71841
Acceso en línea:http://hdl.handle.net/10230/71841
http://dx.doi.org/10.3389/fcomp.2024.1476996
Access Level:acceso abierto
Palabra clave:Complex networks
Music
Symbolic drum patterns
Network similarity
Genre classification
Music generation
Music information representation
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spelling Network representations of drum sequences for classification and generationGómez-Marín, DanielJordà Puig, SergiHerrera, PerfectoComplex networksMusicSymbolic drum patternsNetwork similarityGenre classificationMusic generationMusic information representationComplex networks have emerged as a powerful framework for understanding and analyzing musical compositions, revealing underlying structures and dynamics that may not be immediately apparent. This article explores the application of complex network representations to the study of symbolic drum sequences, a topic that has received limited attention in the literature. The proposed methodology involves encoding drum rhythms as directed, weighted complex networks, where nodes represent drum events, and edges capture the temporal succession of these events. This network-based representation allows for the analysis of similarities between different drumming styles, as well as the generation of novel drum patterns. Through a series of experiments, we demonstrate the effectiveness of this approach. First, we show that the complex network representation can accurately classify drum patterns into their respective musical styles, even with a limited number of training samples. Second, we present a generative model based on Markov chains operating on the network structure, which is able to produce new drum patterns that retain the essential features of the training data. Finally, we validate the perceptual relevance of the generated patterns through listening tests, where participants are unable to distinguish the generated patterns from the original ones, suggesting that the network-based representation effectively captures the underlying characteristics of different drumming styles. The findings of this study have significant implications for music research, genre classification, and generative music applications, highlighting the potential of complex networks to provide a transparent and elegant approach to the analysis and synthesis of rhythmic structures in music.Frontiers202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/71841http://dx.doi.org/10.3389/fcomp.2024.1476996reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésFrontiers in Computer Science. 2025 Jan 21;6:1476996© 2025 Gómez-Marín, Jordà and Herrera. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/718412026-06-12T07:21:37Z
dc.title.none.fl_str_mv Network representations of drum sequences for classification and generation
title Network representations of drum sequences for classification and generation
spellingShingle Network representations of drum sequences for classification and generation
Gómez-Marín, Daniel
Complex networks
Music
Symbolic drum patterns
Network similarity
Genre classification
Music generation
Music information representation
title_short Network representations of drum sequences for classification and generation
title_full Network representations of drum sequences for classification and generation
title_fullStr Network representations of drum sequences for classification and generation
title_full_unstemmed Network representations of drum sequences for classification and generation
title_sort Network representations of drum sequences for classification and generation
dc.creator.none.fl_str_mv Gómez-Marín, Daniel
Jordà Puig, Sergi
Herrera, Perfecto
author Gómez-Marín, Daniel
author_facet Gómez-Marín, Daniel
Jordà Puig, Sergi
Herrera, Perfecto
author_role author
author2 Jordà Puig, Sergi
Herrera, Perfecto
author2_role author
author
dc.subject.none.fl_str_mv Complex networks
Music
Symbolic drum patterns
Network similarity
Genre classification
Music generation
Music information representation
topic Complex networks
Music
Symbolic drum patterns
Network similarity
Genre classification
Music generation
Music information representation
description Complex networks have emerged as a powerful framework for understanding and analyzing musical compositions, revealing underlying structures and dynamics that may not be immediately apparent. This article explores the application of complex network representations to the study of symbolic drum sequences, a topic that has received limited attention in the literature. The proposed methodology involves encoding drum rhythms as directed, weighted complex networks, where nodes represent drum events, and edges capture the temporal succession of these events. This network-based representation allows for the analysis of similarities between different drumming styles, as well as the generation of novel drum patterns. Through a series of experiments, we demonstrate the effectiveness of this approach. First, we show that the complex network representation can accurately classify drum patterns into their respective musical styles, even with a limited number of training samples. Second, we present a generative model based on Markov chains operating on the network structure, which is able to produce new drum patterns that retain the essential features of the training data. Finally, we validate the perceptual relevance of the generated patterns through listening tests, where participants are unable to distinguish the generated patterns from the original ones, suggesting that the network-based representation effectively captures the underlying characteristics of different drumming styles. The findings of this study have significant implications for music research, genre classification, and generative music applications, highlighting the potential of complex networks to provide a transparent and elegant approach to the analysis and synthesis of rhythmic structures in music.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/71841
http://dx.doi.org/10.3389/fcomp.2024.1476996
url http://hdl.handle.net/10230/71841
http://dx.doi.org/10.3389/fcomp.2024.1476996
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Frontiers in Computer Science. 2025 Jan 21;6:1476996
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
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