Automatic tonic identification in Indian art music: approaches and evaluation

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 comp...

Full description

Bibliographic Details
Authors: Gulati, Sankalp, Bellur, Ashwin, Salamon, Justin, Ranjani, H. G., Ishwar, Vignesh, Murthy, Hema A., Serra, Xavier
Format: article
Status:Versión aceptada para publicación
Publication Date:2014
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/25675
Online Access:http://hdl.handle.net/10230/25675
http://dx.doi.org/10.1080/09298215.2013.875042
Access Level:Open access
Keyword:Tonic
Drone
Indian art music
Hindustani
Carnatic
Tanpura
Sadja
Indian classical music
id ES_d20678fca0f99ffd3f888c997d95b657
oai_identifier_str oai:recercat.cat:10230/25675
network_acronym_str ES
network_name_str España
repository_id_str
spelling Automatic tonic identification in Indian art music: approaches and evaluationGulati, SankalpBellur, AshwinSalamon, JustinRanjani, H. G.Ishwar, VigneshMurthy, Hema A.Serra, XavierTonicDroneIndian art musicHindustaniCarnaticTanpuraSadjaIndian classical musicThe 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.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).Taylor & Francis (Routledge)201620162014info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/25675http://dx.doi.org/10.1080/09298215.2013.875042reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésJournal of New Music Research. 2014; 43(1): 55–73.info:eu-repo/grantAgreement/EC/FP7/267583© 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.info:eu-repo/semantics/openAccessoai:recercat.cat:10230/256752026-05-29T05:05:01Z
dc.title.none.fl_str_mv Automatic tonic identification in Indian art music: approaches and evaluation
title Automatic tonic identification in Indian art music: approaches and evaluation
spellingShingle Automatic tonic identification in Indian art music: approaches and evaluation
Gulati, Sankalp
Tonic
Drone
Indian art music
Hindustani
Carnatic
Tanpura
Sadja
Indian classical music
title_short Automatic tonic identification in Indian art music: approaches and evaluation
title_full Automatic tonic identification in Indian art music: approaches and evaluation
title_fullStr Automatic tonic identification in Indian art music: approaches and evaluation
title_full_unstemmed Automatic tonic identification in Indian art music: approaches and evaluation
title_sort Automatic tonic identification in Indian art music: approaches and evaluation
dc.creator.none.fl_str_mv Gulati, Sankalp
Bellur, Ashwin
Salamon, Justin
Ranjani, H. G.
Ishwar, Vignesh
Murthy, Hema A.
Serra, Xavier
author Gulati, Sankalp
author_facet Gulati, Sankalp
Bellur, Ashwin
Salamon, Justin
Ranjani, H. G.
Ishwar, Vignesh
Murthy, Hema A.
Serra, Xavier
author_role author
author2 Bellur, Ashwin
Salamon, Justin
Ranjani, H. G.
Ishwar, Vignesh
Murthy, Hema A.
Serra, Xavier
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Tonic
Drone
Indian art music
Hindustani
Carnatic
Tanpura
Sadja
Indian classical music
topic Tonic
Drone
Indian art music
Hindustani
Carnatic
Tanpura
Sadja
Indian classical music
description 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.
publishDate 2014
dc.date.none.fl_str_mv 2014
2016
2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/25675
http://dx.doi.org/10.1080/09298215.2013.875042
url http://hdl.handle.net/10230/25675
http://dx.doi.org/10.1080/09298215.2013.875042
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of New Music Research. 2014; 43(1): 55–73.
info:eu-repo/grantAgreement/EC/FP7/267583
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis (Routledge)
publisher.none.fl_str_mv Taylor & Francis (Routledge)
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420314002194432
score 15.812429

Similar Items