Finding community structure in music genres networks

Debora Correa, Luciano da F Costa, Alexandre L. M Levada

Research output: Chapter in Book/Conference paperConference paperpeer-review

5 Citations (Scopus)

Abstract

Complex networks have shown to be promising mechanisms to represent several aspects of nature, since their topological and structural features help in the understanding of relations, properties and intrinsic characteristics of the data. In this context, we propose to build music networks in order to find community structures of music genres. Our main contributions are twofold: 1) Define a totally unsupervised approach for music genres discrimination; 2) Incorporate topological features in music data analysis. We compared different distance metrics and clustering algorithms. Each song is represented by a vector of conditional probabilities for the note values in its percussion track. Initial results indicate the effectiveness of the proposed methodology. © 2011 International Society for Music Information Retrieval.
Original languageEnglish
Title of host publication12th International Society for Music Information Retrieval Conference (ISMIR 2011)
EditorsAnssi Klapuri, Colby Leider
Place of PublicationUnites States
PublisherUniversity of Miami
Pages447-452
ISBN (Print)978-061554865-4
Publication statusPublished - 2011
Externally publishedYes
EventISMIR 2011 : 12th International Society for Music Information Retrieval Conference - Miami, United States
Duration: 24 Oct 201128 Oct 2011

Conference

ConferenceISMIR 2011 : 12th International Society for Music Information Retrieval Conference
Country/TerritoryUnited States
CityMiami
Period24/10/1128/10/11

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