Discovering rāga motifs by characterizing communities in networks of melodic patterns

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Gulati, Sankalpca
• dc.contributor.author Serrà Julià, Joanca
• dc.contributor.author Ishwar, Vigneshca
• dc.contributor.author Serra, Xavierca
• dc.date.accessioned 2017-10-09T09:51:03Z
• dc.date.available 2017-10-09T09:51:03Z
• dc.date.issued 2016
• dc.description Comunicació presentada a la 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), celebrada els dies 20 a 25 de març a Xangai, Xina.
• dc.description.abstract Ra̅ga motifs are the main building blocks of the melodic structures in Indian art music. Therefore, the discovery and characterization of such motifs is fundamental for the computational analysis of this music. We propose an approach for discovering ra̅ga motifs from audio music collections. First, we extract melodic patterns from a collection of 44 hours of audio comprising 160 recordings belonging to 10 ra̅gas. Next, we characterize these patterns by performing a network analysis, detecting non-overlapping communities, and exploiting the topological properties of the network to determine a similarity threshold. With that, we select a number of motif candidates that are representative of a ra̅ga, the ra̅ga motifs. For a formal evaluation we perform listening tests with 10 professional musicians. The results indicate that, on an average, the selected melodic phrases correspond to ra̅ga motifs with 85% positive ratings. This opens up the possibilities for many musically-meaningful computational tasks in Indian art music, including human-interpretable ra̅ga recognition, semantic-based music discovery, or pedagogical tools.en
• dc.description.sponsorship This work is partly supported by the European Research Council under the European Unions Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583).
• dc.format.mimetype application/pdfca
• dc.identifier.citation Gulati S, Serrà J, Ishwar V, Serra X. Discovering raga motifs by characterizing communities in networks of melodic patterns. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2016 Mar 20-25; Shanghai, China. [New York]: IEEE; 2016. p. 286-90. DOI: 10.1109/ICASSP.2016.7471682
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP.2016.7471682
• dc.identifier.uri http://hdl.handle.net/10230/32878
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)ca
• dc.relation.ispartof 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2016 Mar 20-25; Shanghai, China. [New York]: IEEE; 2016. p. 286-90.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/267583
• dc.rights © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at http://doi.org/10.1109/ICASSP.2016.7471682
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Carnatic musicen
• dc.subject.keyword Rāga motifen
• dc.subject.keyword Melodic phrasesen
• dc.subject.keyword Melodic similarityen
• dc.subject.keyword Indian art musicen
• dc.title Discovering rāga motifs by characterizing communities in networks of melodic patternsca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion