Symbolic music generation conditioned on continuous-valued emotions
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/21681 |
Resumo: | In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal. We evaluate our approach in a quantitative manner in two ways, first by measuring its note prediction accuracy, and second via a regression task in the valence-arousal plane. Our results demonstrate that our proposed approaches outperform conditioning using control tokens which is representative of the current state of the art. |
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Symbolic music generation conditioned on continuous-valued emotionsDeep LearningSymbolic Music GenerationEmotion-Based Music GenerationTransformersIn this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal. We evaluate our approach in a quantitative manner in two ways, first by measuring its note prediction accuracy, and second via a regression task in the valence-arousal plane. Our results demonstrate that our proposed approaches outperform conditioning using control tokens which is representative of the current state of the art.‘la Caixa’’ Foundation under Grant 100010434 and Grant LCF/BQ/DI19/1173003 - FCT—Foundation for Science and Technology, I.P., through the Project MERGE through the National Funds (PIDDAC) through the Portuguese State Budget under Grant PTDC/CCI-COM/3171/2021 - European Social Fund through the Regional Operational Program Centro 2020 Project CISUC under Grant UID/CEC/00326/2020IEEERepositório Científico do Instituto Politécnico do PortoSulun, SerkanDavies, Matthew E. P.Viana, Paula2023-01-19T12:18:39Z2022-04-222022-04-22T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/21681engS. Sulun, M. E. P. Davies and P. Viana, "Symbolic Music Generation Conditioned on Continuous-Valued Emotions," in IEEE Access, vol. 10, pp. 44617-44626, 2022, doi: 10.1109/ACCESS.2022.3169744.10.1109/ACCESS.2022.3169744info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:17:55Zoai:recipp.ipp.pt:10400.22/21681Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:41:43.145702Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Symbolic music generation conditioned on continuous-valued emotions |
title |
Symbolic music generation conditioned on continuous-valued emotions |
spellingShingle |
Symbolic music generation conditioned on continuous-valued emotions Sulun, Serkan Deep Learning Symbolic Music Generation Emotion-Based Music Generation Transformers |
title_short |
Symbolic music generation conditioned on continuous-valued emotions |
title_full |
Symbolic music generation conditioned on continuous-valued emotions |
title_fullStr |
Symbolic music generation conditioned on continuous-valued emotions |
title_full_unstemmed |
Symbolic music generation conditioned on continuous-valued emotions |
title_sort |
Symbolic music generation conditioned on continuous-valued emotions |
author |
Sulun, Serkan |
author_facet |
Sulun, Serkan Davies, Matthew E. P. Viana, Paula |
author_role |
author |
author2 |
Davies, Matthew E. P. Viana, Paula |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Sulun, Serkan Davies, Matthew E. P. Viana, Paula |
dc.subject.por.fl_str_mv |
Deep Learning Symbolic Music Generation Emotion-Based Music Generation Transformers |
topic |
Deep Learning Symbolic Music Generation Emotion-Based Music Generation Transformers |
description |
In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal. We evaluate our approach in a quantitative manner in two ways, first by measuring its note prediction accuracy, and second via a regression task in the valence-arousal plane. Our results demonstrate that our proposed approaches outperform conditioning using control tokens which is representative of the current state of the art. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-22 2022-04-22T00:00:00Z 2023-01-19T12:18:39Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/21681 |
url |
http://hdl.handle.net/10400.22/21681 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
S. Sulun, M. E. P. Davies and P. Viana, "Symbolic Music Generation Conditioned on Continuous-Valued Emotions," in IEEE Access, vol. 10, pp. 44617-44626, 2022, doi: 10.1109/ACCESS.2022.3169744. 10.1109/ACCESS.2022.3169744 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131504557686784 |