The Matrix profile for motif discovery in audio - an example application in Carnatic music

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• dc.contributor.author Nuttall, Thomas
• dc.contributor.author Plaja-Roglans, Genís
• dc.contributor.author Pearson, Lara
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2022-01-11T08:10:27Z
• dc.date.available 2022-01-11T08:10:27Z
• dc.date.issued 2021
• dc.description Comunicació presentada a: The 15th International Symposium on Computer Music Multidisciplinary Research celebrat del 15 al 19 de novembre de 2021 de manera virtual.
• dc.description.abstract We present here a pipeline for the automated discovery of repeated motifs in audio. Our approach relies on state-of-the-art source separation, pre- dominant pitch extraction and time series motif detection via the matrix profile. Owing to the appropriateness of this approach for the task of motif recognition in the Carnatic musical style of South India, and with access to the recently re- leased Saraga Dataset of Indian Art Music, we provide an example application on a recording of a performance in the Carnatic r ̄aga, R ̄itigaul.a, finding 56 dis- tinct patterns of varying lengths that occur at least 3 times in the recording. The authors include a discussion of the potential musicological significance of this motif finding approach in relation to the particular tradition and beyond.
• dc.description.sponsorship This research was funded by the MUSICAL AI project (PID2019- 111403GB-I00) granted by the Ministry of Science and Innovation of the Spanish Gov- ernment. We also thank Rafael Caro Repetto for his continued guidance and input.
• dc.format.mimetype application/pdf
• dc.identifier.citation Nuttall T, Plaja G, Pearson L, Serra X. The Matrix profile for motif discovery in audio - an example application in Carnatic music.n: Kitahara T, Aramaki M, Kronland-Martinet R, Ystad S, editors. Music in the AI Era. Proceedings of the 15th International Symposium on Computer Music Multidisciplinary Research; 2021 Nov 15-19; Tokyo, Japan. Japan: CMMR 2021 Organizing Committee, The Laboratory PRISM; 2021. p. 109-18.
• dc.identifier.isbn 979-10-97-498-02-3
• dc.identifier.uri http://hdl.handle.net/10230/52182
• dc.language.iso eng
• dc.publisher Les éditions de PRISM
• dc.relation.ispartof Kitahara T, Aramaki M, Kronland-Martinet R, Ystad S, editors. Music in the AI Era. Proceedings of the 15th International Symposium on Computer Music Multidisciplinary Research; 2021 Nov 15-19; Tokyo, Japan. Japan: CMMR 2021 Organizing Committee, The Laboratory PRISM; 2021.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-111403GB-I00
• dc.rights © The Authors
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Musical Pattern Discovery
• dc.subject.keyword Motif Discovery
• dc.subject.keyword Matrix Profile
• dc.subject.keyword Predominant Pitch Extraction
• dc.subject.keyword Carnatic Music
• dc.subject.keyword Indian Art Music
• dc.title The Matrix profile for motif discovery in audio - an example application in Carnatic music
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion