Playing with Cases: Rendering Expressive Music with Case-Based Reasoning

This article surveys long-term research on the problem of rendering expressive music by means of AI techniques with an emphasis on case-based reasoning (CBR). Following a brief overview discussing why people prefer listening to expressive music instead of nonexpressive synthesized music, we examine...

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Detalles Bibliográficos
Autores: López de Mántaras, Ramón, Arcos Rosell, Josep Lluís
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2012
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/137826
Acceso en línea:http://hdl.handle.net/10261/137826
Access Level:acceso abierto
Palabra clave:Audio information
Musical performance
AI techniques
Representative selection
Case-based reasoning systems
Case-based reasoning
Music performance
Music renderings
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spelling Playing with Cases: Rendering Expressive Music with Case-Based ReasoningLópez de Mántaras, RamónArcos Rosell, Josep LluísAudio informationMusical performanceAI techniquesRepresentative selectionCase-based reasoning systemsCase-based reasoningMusic performanceMusic renderingsThis article surveys long-term research on the problem of rendering expressive music by means of AI techniques with an emphasis on case-based reasoning (CBR). Following a brief overview discussing why people prefer listening to expressive music instead of nonexpressive synthesized music, we examine a representative selection of well-known approaches to expressive computer,music performance with an emphasis on AI-related approaches. In the main part of the article we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on Tempo-Express, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting of complementing audio information with information about the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This article is based on the >2011 Robert S. Engelmore Memorial Lecture> given by the first author at AAAI/IAAI 2011.This research is partially supported by the Ministry of Science and Innovation of Spain under the project NEXT-CBR (TIN2009-13692-C03-01) and the Generalitat de Catalunya AGAUR Grant 2009-SGR-1434Peer ReviewedAmerican Association for Artificial IntelligenceGeneralitat de CatalunyaMinisterio de Ciencia e Innovación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2016201620122016info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/137826reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1378262026-05-22T06:33:51Z
dc.title.none.fl_str_mv Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
title Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
spellingShingle Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
López de Mántaras, Ramón
Audio information
Musical performance
AI techniques
Representative selection
Case-based reasoning systems
Case-based reasoning
Music performance
Music renderings
title_short Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
title_full Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
title_fullStr Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
title_full_unstemmed Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
title_sort Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
dc.creator.none.fl_str_mv López de Mántaras, Ramón
Arcos Rosell, Josep Lluís
author López de Mántaras, Ramón
author_facet López de Mántaras, Ramón
Arcos Rosell, Josep Lluís
author_role author
author2 Arcos Rosell, Josep Lluís
author2_role author
dc.contributor.none.fl_str_mv Generalitat de Catalunya
Ministerio de Ciencia e Innovación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Audio information
Musical performance
AI techniques
Representative selection
Case-based reasoning systems
Case-based reasoning
Music performance
Music renderings
topic Audio information
Musical performance
AI techniques
Representative selection
Case-based reasoning systems
Case-based reasoning
Music performance
Music renderings
description This article surveys long-term research on the problem of rendering expressive music by means of AI techniques with an emphasis on case-based reasoning (CBR). Following a brief overview discussing why people prefer listening to expressive music instead of nonexpressive synthesized music, we examine a representative selection of well-known approaches to expressive computer,music performance with an emphasis on AI-related approaches. In the main part of the article we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on Tempo-Express, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting of complementing audio information with information about the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This article is based on the >2011 Robert S. Engelmore Memorial Lecture> given by the first author at AAAI/IAAI 2011.
publishDate 2012
dc.date.none.fl_str_mv 2012
2016
2016
2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/137826
url http://hdl.handle.net/10261/137826
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv American Association for Artificial Intelligence
publisher.none.fl_str_mv American Association for Artificial Intelligence
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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