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Discovering rāga motifs by characterizing communities in networks of melodic patterns

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dc.contributor.author Gulati, Sankalp
dc.contributor.author Serrà Julià, Joan
dc.contributor.author Ishwar, Vignesh
dc.contributor.author Serra, Xavier
dc.date.accessioned 2017-10-09T09:51:03Z
dc.date.available 2017-10-09T09:51:03Z
dc.date.issued 2016
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.uri http://hdl.handle.net/10230/32878
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.
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/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
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.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.title Discovering rāga motifs by characterizing communities in networks of melodic patterns
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1109/ICASSP.2016.7471682
dc.subject.keyword Carnatic music
dc.subject.keyword Rāga motif
dc.subject.keyword Melodic phrases
dc.subject.keyword Melodic similarity
dc.subject.keyword Indian art music
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/267583
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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