A composition algorithm based on crossmodal taste-music correspondences

Autores
Mesz, B.; Sigman, M.; Trevisan, M.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
While there is broad consensus about the structural similarities between language and music, comparably less attention has been devoted to semantic correspondences between these two ubiquitous manifestations of human culture. We have investigated the relations between music and a narrow and bounded domain of semantics: the words and concepts referring to taste sensations. In a recent work, we found that taste words were consistently mapped to musical parameters. Bitter is associated with low-pitched and continuous music (legato), salty is characterized by silences between notes (staccato), sour is high pitched, dissonant and fast and sweet is consonant, slow and soft (Mesz2011). Here we extended these ideas, in a synergistic dialog between music and science, investigating whether music can be algorithmically generated from taste-words. We developed and implemented an algorithm that exploits a large corpus of classic and popular songs. New musical pieces were produced by choosing fragments from the corpus and modifying them to minimize their distance to the region in musical space that characterizes each taste. In order to test the capability of the produced music to elicit significant associations with the different tastes, musical pieces were produced and judged by a group of non musicians. Results showed that participants could decode well above chance the taste-word of the composition. We also discuss how our findings can be expressed in a performance bridging music and cognitive science. © 2012 Mesz, Sigman and Trevisan.
Fil:Mesz, B. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Sigman, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Trevisan, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fuente
Front. Human Neurosci. 2012(MARCH 2012)
Materia
Algorithm
Composition
Cross-modal
Language
Music
Semantics
Taste
adult
article
association
auditory discrimination
auditory feedback
auditory stimulation
bitter taste
controlled study
female
gesture
human
human experiment
language processing
learning algorithm
male
music
normal human
process development
scoring system
semantics
stimulus response
sweetness
task performance
taste discrimination
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
paperaa:paper_16625161_v_nMARCH2012_p_Mesz
Acceder
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oai_identifier_str paperaa:paper_16625161_v_nMARCH2012_p_Mesz
network_acronym_str BDUBAFCEN
repository_id_str 1896
network_name_str Biblioteca Digital (UBA-FCEN)
spelling A composition algorithm based on crossmodal taste-music correspondencesMesz, B.Sigman, M.Trevisan, M.AlgorithmCompositionCross-modalLanguageMusicSemanticsTasteadultarticleassociationauditory discriminationauditory feedbackauditory stimulationbitter tastecontrolled studyfemalegesturehumanhuman experimentlanguage processinglearning algorithmmalemusicnormal humanprocess developmentscoring systemsemanticsstimulus responsesweetnesstask performancetaste discriminationWhile there is broad consensus about the structural similarities between language and music, comparably less attention has been devoted to semantic correspondences between these two ubiquitous manifestations of human culture. We have investigated the relations between music and a narrow and bounded domain of semantics: the words and concepts referring to taste sensations. In a recent work, we found that taste words were consistently mapped to musical parameters. Bitter is associated with low-pitched and continuous music (legato), salty is characterized by silences between notes (staccato), sour is high pitched, dissonant and fast and sweet is consonant, slow and soft (Mesz2011). Here we extended these ideas, in a synergistic dialog between music and science, investigating whether music can be algorithmically generated from taste-words. We developed and implemented an algorithm that exploits a large corpus of classic and popular songs. New musical pieces were produced by choosing fragments from the corpus and modifying them to minimize their distance to the region in musical space that characterizes each taste. In order to test the capability of the produced music to elicit significant associations with the different tastes, musical pieces were produced and judged by a group of non musicians. Results showed that participants could decode well above chance the taste-word of the composition. We also discuss how our findings can be expressed in a performance bridging music and cognitive science. © 2012 Mesz, Sigman and Trevisan.Fil:Mesz, B. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Sigman, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil:Trevisan, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12110/paper_16625161_v_nMARCH2012_p_MeszFront. Human Neurosci. 2012(MARCH 2012)reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/2.5/ar2025-09-29T13:42:54Zpaperaa:paper_16625161_v_nMARCH2012_p_MeszInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-09-29 13:42:55.895Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv A composition algorithm based on crossmodal taste-music correspondences
title A composition algorithm based on crossmodal taste-music correspondences
spellingShingle A composition algorithm based on crossmodal taste-music correspondences
Mesz, B.
Algorithm
Composition
Cross-modal
Language
Music
Semantics
Taste
adult
article
association
auditory discrimination
auditory feedback
auditory stimulation
bitter taste
controlled study
female
gesture
human
human experiment
language processing
learning algorithm
male
music
normal human
process development
scoring system
semantics
stimulus response
sweetness
task performance
taste discrimination
title_short A composition algorithm based on crossmodal taste-music correspondences
title_full A composition algorithm based on crossmodal taste-music correspondences
title_fullStr A composition algorithm based on crossmodal taste-music correspondences
title_full_unstemmed A composition algorithm based on crossmodal taste-music correspondences
title_sort A composition algorithm based on crossmodal taste-music correspondences
dc.creator.none.fl_str_mv Mesz, B.
Sigman, M.
Trevisan, M.
author Mesz, B.
author_facet Mesz, B.
Sigman, M.
Trevisan, M.
author_role author
author2 Sigman, M.
Trevisan, M.
author2_role author
author
dc.subject.none.fl_str_mv Algorithm
Composition
Cross-modal
Language
Music
Semantics
Taste
adult
article
association
auditory discrimination
auditory feedback
auditory stimulation
bitter taste
controlled study
female
gesture
human
human experiment
language processing
learning algorithm
male
music
normal human
process development
scoring system
semantics
stimulus response
sweetness
task performance
taste discrimination
topic Algorithm
Composition
Cross-modal
Language
Music
Semantics
Taste
adult
article
association
auditory discrimination
auditory feedback
auditory stimulation
bitter taste
controlled study
female
gesture
human
human experiment
language processing
learning algorithm
male
music
normal human
process development
scoring system
semantics
stimulus response
sweetness
task performance
taste discrimination
dc.description.none.fl_txt_mv While there is broad consensus about the structural similarities between language and music, comparably less attention has been devoted to semantic correspondences between these two ubiquitous manifestations of human culture. We have investigated the relations between music and a narrow and bounded domain of semantics: the words and concepts referring to taste sensations. In a recent work, we found that taste words were consistently mapped to musical parameters. Bitter is associated with low-pitched and continuous music (legato), salty is characterized by silences between notes (staccato), sour is high pitched, dissonant and fast and sweet is consonant, slow and soft (Mesz2011). Here we extended these ideas, in a synergistic dialog between music and science, investigating whether music can be algorithmically generated from taste-words. We developed and implemented an algorithm that exploits a large corpus of classic and popular songs. New musical pieces were produced by choosing fragments from the corpus and modifying them to minimize their distance to the region in musical space that characterizes each taste. In order to test the capability of the produced music to elicit significant associations with the different tastes, musical pieces were produced and judged by a group of non musicians. Results showed that participants could decode well above chance the taste-word of the composition. We also discuss how our findings can be expressed in a performance bridging music and cognitive science. © 2012 Mesz, Sigman and Trevisan.
Fil:Mesz, B. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Sigman, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Trevisan, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
description While there is broad consensus about the structural similarities between language and music, comparably less attention has been devoted to semantic correspondences between these two ubiquitous manifestations of human culture. We have investigated the relations between music and a narrow and bounded domain of semantics: the words and concepts referring to taste sensations. In a recent work, we found that taste words were consistently mapped to musical parameters. Bitter is associated with low-pitched and continuous music (legato), salty is characterized by silences between notes (staccato), sour is high pitched, dissonant and fast and sweet is consonant, slow and soft (Mesz2011). Here we extended these ideas, in a synergistic dialog between music and science, investigating whether music can be algorithmically generated from taste-words. We developed and implemented an algorithm that exploits a large corpus of classic and popular songs. New musical pieces were produced by choosing fragments from the corpus and modifying them to minimize their distance to the region in musical space that characterizes each taste. In order to test the capability of the produced music to elicit significant associations with the different tastes, musical pieces were produced and judged by a group of non musicians. Results showed that participants could decode well above chance the taste-word of the composition. We also discuss how our findings can be expressed in a performance bridging music and cognitive science. © 2012 Mesz, Sigman and Trevisan.
publishDate 2012
dc.date.none.fl_str_mv 2012
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12110/paper_16625161_v_nMARCH2012_p_Mesz
url http://hdl.handle.net/20.500.12110/paper_16625161_v_nMARCH2012_p_Mesz
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Front. Human Neurosci. 2012(MARCH 2012)
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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score 13.070432

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