Perceptual basis of evolving Western musical styles
- Autores
- Shifres, Favio; Rodríguez Zivic, Pablo H.; Cecchi, Guillermo A.
- Año de publicación
- 2013
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The brain processes temporal statistics to predict future events and to categorize perceptual objects. These statistics, called expectan- cies, are found in music perception, and they span a variety of different features and time scales. Specifically, there is evidence that music perception involves strong expectancies regarding the distri- bution of a melodic interval, namely, the distance between two consecutive notes within the context of another. The recent availability of a large Western music dataset, consisting of the historical record condensed as melodic interval counts, has opened new possibilities for data-driven analysis of musical perception. In this context, we present an analytical approach that, based on cognitive theories of music expectation and machine learning techniques, recovers a set of factors that accurately identifies historical trends and stylistic transitions between the Baroque, Classical, Romantic, and Post-Romantic periods. We also offer a plausible musicological and cognitive interpretation of these factors, allowing us to propose them as data-driven principles of melodic expectation.
Facultad de Bellas Artes - Materia
-
Bellas Artes
Música
pattern recognition; psychology; computational cognition; culturomics - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-nd/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/53310
Ver los metadatos del registro completo
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Perceptual basis of evolving Western musical stylesShifres, FavioRodríguez Zivic, Pablo H.Cecchi, Guillermo A.Bellas ArtesMúsicapattern recognition; psychology; computational cognition; culturomicsThe brain processes temporal statistics to predict future events and to categorize perceptual objects. These statistics, called expectan- cies, are found in music perception, and they span a variety of different features and time scales. Specifically, there is evidence that music perception involves strong expectancies regarding the distri- bution of a melodic interval, namely, the distance between two consecutive notes within the context of another. The recent availability of a large Western music dataset, consisting of the historical record condensed as melodic interval counts, has opened new possibilities for data-driven analysis of musical perception. In this context, we present an analytical approach that, based on cognitive theories of music expectation and machine learning techniques, recovers a set of factors that accurately identifies historical trends and stylistic transitions between the Baroque, Classical, Romantic, and Post-Romantic periods. We also offer a plausible musicological and cognitive interpretation of these factors, allowing us to propose them as data-driven principles of melodic expectation.Facultad de Bellas Artes2013-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/53310enginfo:eu-repo/semantics/altIdentifier/url/http://www.pnas.org/content/110/24/10034.full.pdfinfo:eu-repo/semantics/altIdentifier/issn/0027-8424info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:04:59Zoai:sedici.unlp.edu.ar:10915/53310Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:04:59.67SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Perceptual basis of evolving Western musical styles |
title |
Perceptual basis of evolving Western musical styles |
spellingShingle |
Perceptual basis of evolving Western musical styles Shifres, Favio Bellas Artes Música pattern recognition; psychology; computational cognition; culturomics |
title_short |
Perceptual basis of evolving Western musical styles |
title_full |
Perceptual basis of evolving Western musical styles |
title_fullStr |
Perceptual basis of evolving Western musical styles |
title_full_unstemmed |
Perceptual basis of evolving Western musical styles |
title_sort |
Perceptual basis of evolving Western musical styles |
dc.creator.none.fl_str_mv |
Shifres, Favio Rodríguez Zivic, Pablo H. Cecchi, Guillermo A. |
author |
Shifres, Favio |
author_facet |
Shifres, Favio Rodríguez Zivic, Pablo H. Cecchi, Guillermo A. |
author_role |
author |
author2 |
Rodríguez Zivic, Pablo H. Cecchi, Guillermo A. |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Bellas Artes Música pattern recognition; psychology; computational cognition; culturomics |
topic |
Bellas Artes Música pattern recognition; psychology; computational cognition; culturomics |
dc.description.none.fl_txt_mv |
The brain processes temporal statistics to predict future events and to categorize perceptual objects. These statistics, called expectan- cies, are found in music perception, and they span a variety of different features and time scales. Specifically, there is evidence that music perception involves strong expectancies regarding the distri- bution of a melodic interval, namely, the distance between two consecutive notes within the context of another. The recent availability of a large Western music dataset, consisting of the historical record condensed as melodic interval counts, has opened new possibilities for data-driven analysis of musical perception. In this context, we present an analytical approach that, based on cognitive theories of music expectation and machine learning techniques, recovers a set of factors that accurately identifies historical trends and stylistic transitions between the Baroque, Classical, Romantic, and Post-Romantic periods. We also offer a plausible musicological and cognitive interpretation of these factors, allowing us to propose them as data-driven principles of melodic expectation. Facultad de Bellas Artes |
description |
The brain processes temporal statistics to predict future events and to categorize perceptual objects. These statistics, called expectan- cies, are found in music perception, and they span a variety of different features and time scales. Specifically, there is evidence that music perception involves strong expectancies regarding the distri- bution of a melodic interval, namely, the distance between two consecutive notes within the context of another. The recent availability of a large Western music dataset, consisting of the historical record condensed as melodic interval counts, has opened new possibilities for data-driven analysis of musical perception. In this context, we present an analytical approach that, based on cognitive theories of music expectation and machine learning techniques, recovers a set of factors that accurately identifies historical trends and stylistic transitions between the Baroque, Classical, Romantic, and Post-Romantic periods. We also offer a plausible musicological and cognitive interpretation of these factors, allowing us to propose them as data-driven principles of melodic expectation. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-06 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo 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://sedici.unlp.edu.ar/handle/10915/53310 |
url |
http://sedici.unlp.edu.ar/handle/10915/53310 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://www.pnas.org/content/110/24/10034.full.pdf info:eu-repo/semantics/altIdentifier/issn/0027-8424 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) |
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