An analysis of procedural piano music composition with mood templates using genetic algorithms
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Trabalho de conclusão de curso |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/handle/123456789/34183 |
Resumo: | Creating music in an automatic way has been studied since the beginning of artificial intelligence. One of the biggest obstacles of music generation is the vagueness and subjectivity of the mood or emotion transmitted by a music piece. In this work, we experiment with the generation of piano music using template pieces, represented in MIDI format, as a mood directive. We generated a population of random pieces for templates of two opposing moods - happy and sad - and evolved them with a genetic algorithm until their intended mood was close enough to their respective templates. The fitness function that we implemented uses MIDI statistical features to calculate the distance between the given piece and the template. A questionnaire applied to human listeners showed that the generated pieces could overall meet the objective, which was to express the same mood as the template. However, they still sounded computer-generated, probably due to the lack of rhythm regularity and synchronicity. We believe that the weights in the fitness function still need to be balanced, and that new rhythm features could make the pieces sound more natural. |
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Santos, Luísa Rocha de AzevedoMaia, Sílvia Maria Diniz MonteiroSilla Jr, Carlos NascimentoCosta-Abreu, Márjory da2018-12-11T13:55:12Z2021-09-20T11:46:39Z2018-12-11T13:55:12Z2021-09-20T11:46:39Z2018-11-2820180008263SANTOS, Luísa Rocha de Azevedo. An analysis of procedural piano music composition with mood templates using genetic algorithms. 2018. 69 f. Trabalho de Conclusão de Curso (Bacharelado em Ciência da Computação) - Departamento de Informática e Matemática Aplicada, Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2018.https://repositorio.ufrn.br/handle/123456789/34183Universidade Federal do Rio Grande do NorteUFRNBrasilCiência da ComputaçãoAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessMusic CompositionMusic EmotionMusic MoodGenetic AlgorithmProcedural Content GenerationAn analysis of procedural piano music composition with mood templates using genetic algorithmsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bachelorThesisCreating music in an automatic way has been studied since the beginning of artificial intelligence. One of the biggest obstacles of music generation is the vagueness and subjectivity of the mood or emotion transmitted by a music piece. In this work, we experiment with the generation of piano music using template pieces, represented in MIDI format, as a mood directive. We generated a population of random pieces for templates of two opposing moods - happy and sad - and evolved them with a genetic algorithm until their intended mood was close enough to their respective templates. The fitness function that we implemented uses MIDI statistical features to calculate the distance between the given piece and the template. A questionnaire applied to human listeners showed that the generated pieces could overall meet the objective, which was to express the same mood as the template. However, they still sounded computer-generated, probably due to the lack of rhythm regularity and synchronicity. We believe that the weights in the fitness function still need to be balanced, and that new rhythm features could make the pieces sound more natural.engreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALAnAnalysisOfProcedural_Santos_2018.pdfapplication/pdf2405356https://repositorio.ufrn.br/bitstream/123456789/34183/1/AnAnalysisOfProcedural_Santos_2018.pdf8c057c8ff80619f16c1187ea53794172MD51TEXTAnAnalysisOfProcedural_Santos_2018.pdf.txtExtracted texttext/plain101059https://repositorio.ufrn.br/bitstream/123456789/34183/2/AnAnalysisOfProcedural_Santos_2018.pdf.txtbdbaafaddc5a2fc0f20e14d90053a09fMD52CC-LICENSElicense_rdfapplication/octet-stream811https://repositorio.ufrn.br/bitstream/123456789/34183/3/license_rdfe39d27027a6cc9cb039ad269a5db8e34MD53LICENSElicense.txttext/plain1748https://repositorio.ufrn.br/bitstream/123456789/34183/4/license.txt8a4605be74aa9ea9d79846c1fba20a33MD54123456789/341832021-09-20 08:46:39.111oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-09-20T11:46:39Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
An analysis of procedural piano music composition with mood templates using genetic algorithms |
title |
An analysis of procedural piano music composition with mood templates using genetic algorithms |
spellingShingle |
An analysis of procedural piano music composition with mood templates using genetic algorithms Santos, Luísa Rocha de Azevedo Music Composition Music Emotion Music Mood Genetic Algorithm Procedural Content Generation |
title_short |
An analysis of procedural piano music composition with mood templates using genetic algorithms |
title_full |
An analysis of procedural piano music composition with mood templates using genetic algorithms |
title_fullStr |
An analysis of procedural piano music composition with mood templates using genetic algorithms |
title_full_unstemmed |
An analysis of procedural piano music composition with mood templates using genetic algorithms |
title_sort |
An analysis of procedural piano music composition with mood templates using genetic algorithms |
author |
Santos, Luísa Rocha de Azevedo |
author_facet |
Santos, Luísa Rocha de Azevedo |
author_role |
author |
dc.contributor.referees1.none.fl_str_mv |
Maia, Sílvia Maria Diniz Monteiro |
dc.contributor.referees2.none.fl_str_mv |
Silla Jr, Carlos Nascimento |
dc.contributor.author.fl_str_mv |
Santos, Luísa Rocha de Azevedo |
dc.contributor.advisor1.fl_str_mv |
Costa-Abreu, Márjory da |
contributor_str_mv |
Costa-Abreu, Márjory da |
dc.subject.por.fl_str_mv |
Music Composition Music Emotion Music Mood Genetic Algorithm Procedural Content Generation |
topic |
Music Composition Music Emotion Music Mood Genetic Algorithm Procedural Content Generation |
description |
Creating music in an automatic way has been studied since the beginning of artificial intelligence. One of the biggest obstacles of music generation is the vagueness and subjectivity of the mood or emotion transmitted by a music piece. In this work, we experiment with the generation of piano music using template pieces, represented in MIDI format, as a mood directive. We generated a population of random pieces for templates of two opposing moods - happy and sad - and evolved them with a genetic algorithm until their intended mood was close enough to their respective templates. The fitness function that we implemented uses MIDI statistical features to calculate the distance between the given piece and the template. A questionnaire applied to human listeners showed that the generated pieces could overall meet the objective, which was to express the same mood as the template. However, they still sounded computer-generated, probably due to the lack of rhythm regularity and synchronicity. We believe that the weights in the fitness function still need to be balanced, and that new rhythm features could make the pieces sound more natural. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-12-11T13:55:12Z 2021-09-20T11:46:39Z |
dc.date.available.fl_str_mv |
2018-12-11T13:55:12Z 2021-09-20T11:46:39Z |
dc.date.issued.fl_str_mv |
2018-11-28 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
format |
bachelorThesis |
status_str |
publishedVersion |
dc.identifier.pt_BR.fl_str_mv |
20180008263 |
dc.identifier.citation.fl_str_mv |
SANTOS, Luísa Rocha de Azevedo. An analysis of procedural piano music composition with mood templates using genetic algorithms. 2018. 69 f. Trabalho de Conclusão de Curso (Bacharelado em Ciência da Computação) - Departamento de Informática e Matemática Aplicada, Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2018. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/handle/123456789/34183 |
identifier_str_mv |
20180008263 SANTOS, Luísa Rocha de Azevedo. An analysis of procedural piano music composition with mood templates using genetic algorithms. 2018. 69 f. Trabalho de Conclusão de Curso (Bacharelado em Ciência da Computação) - Departamento de Informática e Matemática Aplicada, Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2018. |
url |
https://repositorio.ufrn.br/handle/123456789/34183 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal do Rio Grande do Norte |
dc.publisher.initials.fl_str_mv |
UFRN |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Ciência da Computação |
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Universidade Federal do Rio Grande do Norte |
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