Automatic tonic identification in Indian art music: approaches and evaluation
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Gulati, Sankalpca
- dc.contributor.author Bellur, Ashwinca
- dc.contributor.author Salamon, Justinca
- dc.contributor.author Ranjani, H. G.ca
- dc.contributor.author Ishwar, Vigneshca
- dc.contributor.author Murthy, Hema A.ca
- dc.contributor.author Serra, Xavierca
- dc.date.accessioned 2016-01-28T08:12:30Z
- dc.date.available 2016-01-28T08:12:30Z
- dc.date.issued 2014ca
- dc.description.abstract The tonic is a fundamental concept in Indian art music. It is the base pitch, which an artist chooses in order to construct the melodies during a rāg(a) rendition, and all accompanying instruments are tuned using the tonic pitch. Consequently, tonic identification is a fundamental task for most computational analyses of Indian art music, such as intonation analysis, melodic motif analysis and rāg recognition. In this paper we review existing approaches for tonic identification in Indian art music and evaluate them on six diverse datasets for a thorough comparison and analysis. We study the performance of each method in different contexts such as the presence/absence of additional metadata, the quality of audio data, the duration of audio data, music tradition (Hindustani/Carnatic) and the gender of the singer (male/female). We show that the approaches that combine multi-pitch analysis with machine learning provide the best performance in most cases (90% identification accuracy on average), and are robust across the aforementioned contexts compared to the approaches based on expert knowledge. In addition, we also show that the performance of the latter can be improved when additional metadata is available to further constrain the problem. Finally, we present a detailed error analysis of each method, providing further insights into the advantages and limitations of the methods.
- dc.description.sponsorship This work is partly supported by the European Research Council/nunder the European Union’s Seventh Framework Program, as/npart of the CompMusic project (ERC grant agreement 267583).
- dc.format.mimetype application/pdfca
- dc.identifier.citation Gulati S, Bellur A, Salamon J, Ranjani HG, Ishwar V, Murthy HA, Serra X. Automatic tonic identification in Indian art music: approaches and evaluation. Journal of New Music Research. 2014; 43(1): 55–73. DOI 10.1080/09298215.2013.875042ca
- dc.identifier.doi http://dx.doi.org/10.1080/09298215.2013.875042
- dc.identifier.issn 0929-8215ca
- dc.identifier.uri http://hdl.handle.net/10230/25675
- dc.language.iso engca
- dc.publisher Taylor & Francis (Routledge)ca
- dc.relation.ispartof Journal of New Music Research. 2014; 43(1): 55–73.
- dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/267583ca
- dc.rights © Taylor & Francis. This is an electronic version of an article published in [Gulati S, Bellur A, Salamon J, Ranjani HG, Ishwar V, Murthy HA, Serra X. Automatic tonic identification in Indian art music: approaches and evaluation. Journal of New Music Research. 2014;43(01):55–73.]. [Journal of New Music Research] is available online at: http://www.tandfonline.com/doi/abs/10.1080/09298215.2013.875042.ca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.subject.keyword Tonic
- dc.subject.keyword Drone
- dc.subject.keyword Indian art music
- dc.subject.keyword Hindustani
- dc.subject.keyword Carnatic
- dc.subject.keyword Tanpura
- dc.subject.keyword Sadja
- dc.subject.keyword Indian classical music
- dc.title Automatic tonic identification in Indian art music: approaches and evaluationca
- dc.type info:eu-repo/semantics/articleca
- dc.type.version info:eu-repo/semantics/acceptedVersionca