An approach to predicting bowing control parameter contours in violin performance

We present a machine learning approach to modeling bowing control parameter/ncontours in violin performance. Using accurate sensing techniques/nwe obtain relevant timbre-related bowing control parameters such as bow/ntransversal velocity, bow pressing force, and bow-bridge distance of each/nperforme...

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Detalhes bibliográficos
Autores: Maestre Gómez, Esteban, Ramírez, Rafael, 1966-
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2009
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/12399
Acesso em linha:http://hdl.handle.net/10230/12399
http://dx.doi.org/10.3233/IDA-2010-0441
Access Level:acceso abierto
Palavra-chave:Violí, Música per a
So -- Tractament per ordinador
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spelling An approach to predicting bowing control parameter contours in violin performanceMaestre Gómez, EstebanRamírez, Rafael, 1966-Violí, Música per aSo -- Tractament per ordinadorWe present a machine learning approach to modeling bowing control parameter/ncontours in violin performance. Using accurate sensing techniques/nwe obtain relevant timbre-related bowing control parameters such as bow/ntransversal velocity, bow pressing force, and bow-bridge distance of each/nperformed note. Each performed note is represented by a curve parameter/nvector and a number of note classes are defined. The principal components/nof the data represented by the set of curve parameter vectors are obtained/nfor each class. Once curve parameter vectors are expressed in the new space/ndefined by the principal components, we train a model based on inductive/nlogic programming, able to predict curve parameter vectors used for rendering/nbowing controls. We evaluate the prediction results and show the potential/nof the model by predicting bowing control parameter contours from an/nannotated input score.IOS Press201120112009info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/12399http://dx.doi.org/10.3233/IDA-2010-0441reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésIntelligent Data Anaylisis. 2010; 14(5): 587-599info:eu-repo/grantAgreement/EC/FP7/215749© 2010 – IOS Press, Esteban Maestre Gómez and Rafael Ramírezinfo:eu-repo/semantics/openAccessoai:recercat.cat:10230/123992026-05-29T05:05:01Z
dc.title.none.fl_str_mv An approach to predicting bowing control parameter contours in violin performance
title An approach to predicting bowing control parameter contours in violin performance
spellingShingle An approach to predicting bowing control parameter contours in violin performance
Maestre Gómez, Esteban
Violí, Música per a
So -- Tractament per ordinador
title_short An approach to predicting bowing control parameter contours in violin performance
title_full An approach to predicting bowing control parameter contours in violin performance
title_fullStr An approach to predicting bowing control parameter contours in violin performance
title_full_unstemmed An approach to predicting bowing control parameter contours in violin performance
title_sort An approach to predicting bowing control parameter contours in violin performance
dc.creator.none.fl_str_mv Maestre Gómez, Esteban
Ramírez, Rafael, 1966-
author Maestre Gómez, Esteban
author_facet Maestre Gómez, Esteban
Ramírez, Rafael, 1966-
author_role author
author2 Ramírez, Rafael, 1966-
author2_role author
dc.subject.none.fl_str_mv Violí, Música per a
So -- Tractament per ordinador
topic Violí, Música per a
So -- Tractament per ordinador
description We present a machine learning approach to modeling bowing control parameter/ncontours in violin performance. Using accurate sensing techniques/nwe obtain relevant timbre-related bowing control parameters such as bow/ntransversal velocity, bow pressing force, and bow-bridge distance of each/nperformed note. Each performed note is represented by a curve parameter/nvector and a number of note classes are defined. The principal components/nof the data represented by the set of curve parameter vectors are obtained/nfor each class. Once curve parameter vectors are expressed in the new space/ndefined by the principal components, we train a model based on inductive/nlogic programming, able to predict curve parameter vectors used for rendering/nbowing controls. We evaluate the prediction results and show the potential/nof the model by predicting bowing control parameter contours from an/nannotated input score.
publishDate 2009
dc.date.none.fl_str_mv 2009
2011
2011
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/10230/12399
http://dx.doi.org/10.3233/IDA-2010-0441
url http://hdl.handle.net/10230/12399
http://dx.doi.org/10.3233/IDA-2010-0441
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Intelligent Data Anaylisis. 2010; 14(5): 587-599
info:eu-repo/grantAgreement/EC/FP7/215749
dc.rights.none.fl_str_mv © 2010 – IOS Press, Esteban Maestre Gómez and Rafael Ramírez
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2010 – IOS Press, Esteban Maestre Gómez and Rafael Ramírez
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IOS Press
publisher.none.fl_str_mv IOS Press
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
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score 15.812429

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