Phrase-based rāga recognition using vector space modeling

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• dc.contributor.author Gulati, Sankalpca
• dc.contributor.author Serrà Julià, Joanca
• dc.contributor.author Ishwar, Vigneshca
• dc.contributor.author Sentürk, Sertanca
• dc.contributor.author Serra, Xavierca
• dc.date.accessioned 2017-10-09T09:51:08Z
• dc.date.available 2017-10-09T09:51:08Z
• 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 Automatic raga recognition is one of the fundamental computational tasks in Indian art music. Motivated by the way seasoned listeners identify ragas, we propose a raga recognition approach based on melodic phrases. Firstly, we extract melodic patterns from a collection of audio recordings in an unsupervised way. Next, we group similar patterns by exploiting complex networks concepts and techniques. Drawing an analogy to topic modeling in text classification, we then represent audio recordings using a vector space model. Finally, we employ a number of classification strategies to build a predictive model for raga recognition. To evaluate our approach, we compile a music collection of over 124 hours, comprising 480 recordings and 40 ragas. We obtain 70% accuracy with the full 40-raga collection, and up to 92% accuracy with its 10-raga subset. We show that phrase-based raga recognition is a successful strategy, on par with the state of the art, and sometimes outperforms it. A by-product of our approach, which arguably is as important as the task of raga recognition, is the identification of raga-phrases. These phrases can be used as a dictionary of semantically-meaningful melodic units for several computational tasks in Indian art music.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, Sentürk S, Serra X. Phrase-based raga recognition using vector space modeling. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2016 Mar 20-25; Shanghai, China. [New York]: IEEE; 2016. p. 66-70. DOI: 10.1109/ICASSP.2016.7471638
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP.2016.7471638
• dc.identifier.uri http://hdl.handle.net/10230/32879
• 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. 66-70.
• 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.2016.7471638
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Carnatic musicen
• dc.subject.keyword Rāga recognitionen
• dc.subject.keyword Rāga motifsen
• dc.subject.keyword Melodic phrasesen
• dc.subject.keyword Indian art musicen
• dc.title Phrase-based rāga recognition using vector space modelingca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion