A composition algorithm based on crossmodal taste-music correspondences

Autores
Mesz, Bruno; Sigman, Mariano; Trevisan, Marcos Alberto
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 (Mesz et al., 2011). 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.
Fil: Mesz, Bruno. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Acústica y Percepción Sonora; Argentina
Fil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; Argentina
Fil: Trevisan, Marcos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Sistemas Dinámicos; Argentina
Materia
Music taste
Musical algorithm
Semantics
Cross-modal associations
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/268854
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A composition algorithm based on crossmodal taste-music correspondencesMesz, BrunoSigman, MarianoTrevisan, Marcos AlbertoMusic tasteMusical algorithmSemanticsCross-modal associationshttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1While 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 (Mesz et al., 2011). 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.Fil: Mesz, Bruno. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Acústica y Percepción Sonora; ArgentinaFil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; ArgentinaFil: Trevisan, Marcos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Sistemas Dinámicos; ArgentinaFrontiers Media2012-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/268854Mesz, Bruno; Sigman, Mariano; Trevisan, Marcos Alberto; A composition algorithm based on crossmodal taste-music correspondences; Frontiers Media; Frontiers In Human Neuroscience; 6; 4-2012; 1-61662-5161CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2012.00071/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fnhum.2012.00071info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:21:42Zoai:ri.conicet.gov.ar:11336/268854instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:21:43.24CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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, Bruno
Music taste
Musical algorithm
Semantics
Cross-modal associations
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, Bruno
Sigman, Mariano
Trevisan, Marcos Alberto
author Mesz, Bruno
author_facet Mesz, Bruno
Sigman, Mariano
Trevisan, Marcos Alberto
author_role author
author2 Sigman, Mariano
Trevisan, Marcos Alberto
author2_role author
author
dc.subject.none.fl_str_mv Music taste
Musical algorithm
Semantics
Cross-modal associations
topic Music taste
Musical algorithm
Semantics
Cross-modal associations
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
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 (Mesz et al., 2011). 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.
Fil: Mesz, Bruno. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Acústica y Percepción Sonora; Argentina
Fil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; Argentina
Fil: Trevisan, Marcos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Sistemas Dinámicos; 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 (Mesz et al., 2011). 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.
publishDate 2012
dc.date.none.fl_str_mv 2012-04
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/11336/268854
Mesz, Bruno; Sigman, Mariano; Trevisan, Marcos Alberto; A composition algorithm based on crossmodal taste-music correspondences; Frontiers Media; Frontiers In Human Neuroscience; 6; 4-2012; 1-6
1662-5161
CONICET Digital
CONICET
url http://hdl.handle.net/11336/268854
identifier_str_mv Mesz, Bruno; Sigman, Mariano; Trevisan, Marcos Alberto; A composition algorithm based on crossmodal taste-music correspondences; Frontiers Media; Frontiers In Human Neuroscience; 6; 4-2012; 1-6
1662-5161
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2012.00071/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fnhum.2012.00071
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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