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Phrase-based rāga recognition using vector space modeling

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dc.contributor.author Gulati, Sankalp
dc.contributor.author Serrà Julià, Joan
dc.contributor.author Ishwar, Vignesh
dc.contributor.author Sentürk, Sertan
dc.contributor.author Serra, Xavier
dc.date.accessioned 2017-10-09T09:51:08Z
dc.date.available 2017-10-09T09:51:08Z
dc.date.issued 2016
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.uri http://hdl.handle.net/10230/32879
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.
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. 66-70.
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.title Phrase-based rāga recognition using vector space modeling
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1109/ICASSP.2016.7471638
dc.subject.keyword Carnatic music
dc.subject.keyword Rāga recognition
dc.subject.keyword Rāga motifs
dc.subject.keyword Melodic phrases
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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