Affective computing in the context of music therapy: a systematic review
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/22844 |
Resumo: | Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy. |
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Affective computing in the context of music therapy: a systematic reviewComputación afectiva en el contexto de la musicoterapia: una revisión sistemáticaComputação afetiva no contexto da musicoterapia: uma revisão sistemáticaComputación afectivaReconocimiento de emocionesEstimulacíon acústicaSistema de recomendaciónMusicoterapia.Computação afetivaReconhecimento de emoçõesEstimulação acústicaSistema de recomendaçãoMusicoterapia.Affective ComputingEmotion RecognitionAuditory StimuliRecommendation SystemMusic therapy.Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.La musicoterapia es una herramienta eficaz para ralentizar el progreso de la demencia, ya que la interacción con la música puede evocar emociones que estimulan las áreas del cerebro responsables de la memoria. Esta terapia tiene más éxito cuando el terapeuta proporciona estímulos adecuados y personalizados para cada paciente. Esta personalización suele ser difícil. Por lo tanto, los métodos de Inteligencia Artificial (IA) pueden ayudar en esta tarea. Este artículo trae una revisión sistemática de la literatura en el campo de la computación afectiva en el contexto de la terapia musical. En particular, nuestro objetivo es evaluar los métodos de inteligencia artificial para realizar el reconocimiento automático de emociones aplicado a las interfaces musicales hombre-máquina (HMMI). Para realizar la revisión, realizamos una búsqueda automática en cinco de las principales bases de datos científicas en los campos de la computación inteligente, la ingeniería y la medicina. Buscamos todos los artículos publicados entre 2016 y 2020, cuyos metadatos, título o resumen contengan los términos definidos en la cadena de búsqueda. El protocolo de revisión sistemática resultó en la inclusión de 144 trabajos de las 290 publicaciones devueltas de la búsqueda. A través de esta revisión del estado del arte, fue posible enumerar los desafíos actuales en el reconocimiento automático de emociones. También fue posible darse cuenta del potencial del reconocimiento automático de emociones para construir soluciones de asistencia no invasivas basadas en interfaces musicales hombre-máquina, así como las técnicas de inteligencia artificial que se utilizan en el reconocimiento de emociones a partir de datos multimodal. Por lo tanto, el aprendizaje automático para el reconocimiento de emociones a partir de diferentes fuentes de datos puede ser un enfoque importante para optimizar los objetivos clínicos que se deben lograr a través de la musicoterapia.A musicoterapia é uma ferramenta eficaz para retardar o progresso da demência, uma vez que a interação com a música pode evocar emoções que estimulam as áreas do cérebro responsáveis pela memória. Essa terapia é mais bem-sucedida quando o terapeuta fornece estímulos adequados e personalizados para cada paciente. Essa personalização costuma ser difícil. Assim, métodos de Inteligência Artificial (IA) podem auxiliar nessa tarefa. Este artigo traz uma revisão sistemática da literatura da área de computação afetiva no contexto da musicoterapia. Em particular, pretendemos avaliar métodos de IA para realizar o reconhecimento automático de emoções aplicados a Interfaces Musicais Homem-Máquina (HMMI). Para realizar a revisão, realizamos uma busca automática em cinco das principais bases de dados científicas nas áreas de computação inteligente, engenharia e medicina. Procuramos todos os artigos publicados entre 2016 e 2020, cujos metadados, título ou resumo contenham os termos definidos na string de pesquisa. O protocolo de revisão sistemática resultou na inclusão de 144 trabalhos das 290 publicações retornadas da pesquisa. Através desta revisão do estado da arte, foi possível elencar os desafios atuais no reconhecimento automático de emoções. Também foi possível perceber o potencial do reconhecimento automático de emoções para construir soluções assistivas não invasivas baseadas em interfaces musicais homem-máquina, bem como as técnicas de inteligência artificial em uso no reconhecimento de emoções a partir de dados multimodais. Assim, o aprendizado de máquina para reconhecimento de emoções de diferentes fontes de dados pode ser uma abordagem importante para otimizar os objetivos clínicos a serem alcançados por meio da musicoterapia.Research, Society and Development2021-11-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/2284410.33448/rsd-v10i15.22844Research, Society and Development; Vol. 10 No. 15; e392101522844Research, Society and Development; Vol. 10 Núm. 15; e392101522844Research, Society and Development; v. 10 n. 15; e3921015228442525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/22844/20414Copyright (c) 2021 Maíra Araújo de Santana; Clarisse Lins de Lima; Arianne Sarmento Torcate; Flávio Secco Fonseca; Wellington Pinheiro dos Santoshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSantana, Maíra Araújo deLima, Clarisse Lins deTorcate, Arianne SarmentoFonseca, Flávio SeccoSantos, Wellington Pinheiro dos2021-12-06T10:13:53Zoai:ojs.pkp.sfu.ca:article/22844Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:41:53.423341Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Affective computing in the context of music therapy: a systematic review Computación afectiva en el contexto de la musicoterapia: una revisión sistemática Computação afetiva no contexto da musicoterapia: uma revisão sistemática |
title |
Affective computing in the context of music therapy: a systematic review |
spellingShingle |
Affective computing in the context of music therapy: a systematic review Santana, Maíra Araújo de Computación afectiva Reconocimiento de emociones Estimulacíon acústica Sistema de recomendación Musicoterapia. Computação afetiva Reconhecimento de emoções Estimulação acústica Sistema de recomendação Musicoterapia. Affective Computing Emotion Recognition Auditory Stimuli Recommendation System Music therapy. |
title_short |
Affective computing in the context of music therapy: a systematic review |
title_full |
Affective computing in the context of music therapy: a systematic review |
title_fullStr |
Affective computing in the context of music therapy: a systematic review |
title_full_unstemmed |
Affective computing in the context of music therapy: a systematic review |
title_sort |
Affective computing in the context of music therapy: a systematic review |
author |
Santana, Maíra Araújo de |
author_facet |
Santana, Maíra Araújo de Lima, Clarisse Lins de Torcate, Arianne Sarmento Fonseca, Flávio Secco Santos, Wellington Pinheiro dos |
author_role |
author |
author2 |
Lima, Clarisse Lins de Torcate, Arianne Sarmento Fonseca, Flávio Secco Santos, Wellington Pinheiro dos |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Santana, Maíra Araújo de Lima, Clarisse Lins de Torcate, Arianne Sarmento Fonseca, Flávio Secco Santos, Wellington Pinheiro dos |
dc.subject.por.fl_str_mv |
Computación afectiva Reconocimiento de emociones Estimulacíon acústica Sistema de recomendación Musicoterapia. Computação afetiva Reconhecimento de emoções Estimulação acústica Sistema de recomendação Musicoterapia. Affective Computing Emotion Recognition Auditory Stimuli Recommendation System Music therapy. |
topic |
Computación afectiva Reconocimiento de emociones Estimulacíon acústica Sistema de recomendación Musicoterapia. Computação afetiva Reconhecimento de emoções Estimulação acústica Sistema de recomendação Musicoterapia. Affective Computing Emotion Recognition Auditory Stimuli Recommendation System Music therapy. |
description |
Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-28 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/22844 10.33448/rsd-v10i15.22844 |
url |
https://rsdjournal.org/index.php/rsd/article/view/22844 |
identifier_str_mv |
10.33448/rsd-v10i15.22844 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/22844/20414 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 10 No. 15; e392101522844 Research, Society and Development; Vol. 10 Núm. 15; e392101522844 Research, Society and Development; v. 10 n. 15; e392101522844 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052790571270144 |