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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/268854
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:33:39Zoai: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:34982025-09-29 09:33:39.857CONICET 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 |
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openAccess |
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https://creativecommons.org/licenses/by/2.5/ar/ |
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application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Frontiers Media |
publisher.none.fl_str_mv |
Frontiers Media |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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