MusicGen Music generation model as a tool for artistic creation

The current work is an exploration on how to re purpose AI driven technologies to generate music, in a way that prioritizes the artistic endeavour of musical composition. A particular concept, which is hereby addressed, is the idea of agency of decision in the creative process. The study will center...

Full description

Autores:
Tovar García, Diego Alejandro
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2024
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/73824
Acceso en línea:
https://hdl.handle.net/1992/73824
Palabra clave:
AI
Music
Generative Music
Deep Learning
Machine Learning
Convolusional Autoencoder
Art
Ingeniería
Arte
Música
Rights
embargoedAccess
License
https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
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network_acronym_str UNIANDES2
network_name_str Séneca: repositorio Uniandes
repository_id_str
dc.title.eng.fl_str_mv MusicGen Music generation model as a tool for artistic creation
title MusicGen Music generation model as a tool for artistic creation
spellingShingle MusicGen Music generation model as a tool for artistic creation
AI
Music
Generative Music
Deep Learning
Machine Learning
Convolusional Autoencoder
Art
Ingeniería
Arte
Música
title_short MusicGen Music generation model as a tool for artistic creation
title_full MusicGen Music generation model as a tool for artistic creation
title_fullStr MusicGen Music generation model as a tool for artistic creation
title_full_unstemmed MusicGen Music generation model as a tool for artistic creation
title_sort MusicGen Music generation model as a tool for artistic creation
dc.creator.fl_str_mv Tovar García, Diego Alejandro
dc.contributor.advisor.none.fl_str_mv Manrique Piramanrique, Rubén Francisco
dc.contributor.author.none.fl_str_mv Tovar García, Diego Alejandro
dc.subject.keyword.none.fl_str_mv AI
Music
Generative Music
Deep Learning
Machine Learning
Convolusional Autoencoder
Art
topic AI
Music
Generative Music
Deep Learning
Machine Learning
Convolusional Autoencoder
Art
Ingeniería
Arte
Música
dc.subject.themes.none.fl_str_mv Ingeniería
Arte
Música
description The current work is an exploration on how to re purpose AI driven technologies to generate music, in a way that prioritizes the artistic endeavour of musical composition. A particular concept, which is hereby addressed, is the idea of agency of decision in the creative process. The study will center on the execution of a spatial intervention where the sound experience will be built from user-given-prompts describing the space they roam. The code for the project can be found at https://github.com/Didage/spatial-music-gen.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-02-02T18:13:35Z
dc.date.issued.none.fl_str_mv 2024-02-01
dc.date.accepted.none.fl_str_mv 2024-02-01
dc.type.none.fl_str_mv Trabajo de grado - Pregrado
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.type.content.none.fl_str_mv Text
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dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/1992/73824
dc.identifier.instname.none.fl_str_mv instname:Universidad de los Andes
dc.identifier.reponame.none.fl_str_mv reponame:Repositorio Institucional Séneca
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url https://hdl.handle.net/1992/73824
identifier_str_mv instname:Universidad de los Andes
reponame:Repositorio Institucional Séneca
repourl:https://repositorio.uniandes.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.none.fl_str_mv Copet, J., Kreuk, F., Gat, I., Remez, T., Kant, D., Synnaeve, G.,
Adi, Y., D ́efossez, A.: Simple and controllable music generation (6 2023), http://arxiv.org/abs/2306.05284
Copland, A.: What to listen for in music. New American Library, New York (1953)
D ́efossez, A., Copet, J., Synnaeve, G., Adi, Y.: High fidelity neural audio compres-sion (10 2022), http://arxiv.org/abs/2210.13438
Hadjeres, G., Pachet, F., Nielsen, F.: Deepbach: a steerable model for bach chorales generation (12 2016), http://arxiv.org/abs/1612.01010
Hernandez-Olivan, C., Beltran, J.R.: Music composition with deep learning: A review (8 2021), http://arxiv.org/abs/2108.12290
Huang, A., Wu, R.: Deep learning for music (6 2016), http://arxiv.org/abs/1606.04930
Iosafat, D.: On sonification of place: Psychosonography and urban portrait (4 2009). https://doi.org/10.1017/S1355771809000077
Klein, G.: Site-sounds: On strategies of sound art in public space (4 2009). https://doi.org/10.1017/S1355771809000132
Maurer, J.: A brief history of algorithmic composition (1999), https://ccrma.stanford.edu/ blackrse/algorithm.html
Tittel, C.: Sound art as sonification, and the artistic treatment of features in our surroundings (4 2009). https://doi.org/10.1017/S1355771809000089
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dc.format.extent.none.fl_str_mv 14 páginas
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad de los Andes
dc.publisher.program.none.fl_str_mv Ingeniería de Sistemas y Computación
dc.publisher.faculty.none.fl_str_mv Facultad de Ingeniería
dc.publisher.department.none.fl_str_mv Departamento de Ingeniería Sistemas y Computación
publisher.none.fl_str_mv Universidad de los Andes
institution Universidad de los Andes
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spelling Manrique Piramanrique, Rubén FranciscoTovar García, Diego Alejandro2024-02-02T18:13:35Z2024-02-012024-02-01https://hdl.handle.net/1992/73824instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The current work is an exploration on how to re purpose AI driven technologies to generate music, in a way that prioritizes the artistic endeavour of musical composition. A particular concept, which is hereby addressed, is the idea of agency of decision in the creative process. The study will center on the execution of a spatial intervention where the sound experience will be built from user-given-prompts describing the space they roam. The code for the project can be found at https://github.com/Didage/spatial-music-gen.Ingeniero de Sistemas y ComputaciónPregrado14 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y Computaciónhttps://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfMusicGen Music generation model as a tool for artistic creationTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPAIMusicGenerative MusicDeep LearningMachine LearningConvolusional AutoencoderArtIngenieríaArteMúsicaCopet, J., Kreuk, F., Gat, I., Remez, T., Kant, D., Synnaeve, G.,Adi, Y., D ́efossez, A.: Simple and controllable music generation (6 2023), http://arxiv.org/abs/2306.05284Copland, A.: What to listen for in music. 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