Dimensions on the Perception of Users Towards AI Generated Music
Resumo
Music-generating Artificial Intelligence has reached a point where anyone, musician or not, can generate full-length songs from a simple textual query. As with other recent AI advances, such services are poised to impact various sectors of arts and industry, sparking social debate about their role in our daily lives. In this paper, we investigate the question: How do musicians and non-musicians perceive AI-Generated Music (AGM) services as a tool? We conducted a user-centered study (N=96) exploring perceptions of AGM in composition, production, education, and its ethical implications. The research is based on a quantitative survey analyzed using Exploratory Factor Analysis and Latent Profile Analysis, complemented by a thematic analysis of open-ended responses. Overall, results indicate a positive trend in opinions towards AGM for composition and production. The Latent Profile Analysis reveals two distinct participant groups. The larger group (77%) is highly enthusiastic about AGM for composition and production and also holds a more optimistic view of its ethical and cultural implications. The smaller group (23%) is broadly skeptical, holding a negative view of AGM’s compositional, production, and ethical aspects. Notably, both groups acknowledge the potential of AGM in education. This finding supports AGM as a promising tool for democratizing music composition and education.Referências
Astrid Schepman and Paul Rodway. Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports, 1:100014, 2020.
Ali Akbar Septiandri, Marios Constantinides, and Daniele Quercia. The potential impact of ai innovations on us occupations. PNAS nexus, 3(9):pgae320, 2024.
Florian Grötschla, Ahmet Solak, Luca A Lanzendörfer, and Roger Wattenhofer. Benchmarking music generation models and metrics via human preference studies. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE, 2025.
Dinh-Viet-Toan Le, Louis Bigo, Dorien Herremans, and Mikaela Keller. Natural language processing methods for symbolic music generation and information retrieval: a survey. ACM Computing Surveys, 57(7):1–40, 2025.
Yujia Zhao, Mingzhi Yang, Yujia Lin, Xiaohong Zhang, Feifei Shi, Zongjie Wang, Jianguo Ding, and Huansheng Ning. Ai-enabled text-to-music generation: A comprehensive review of methods, frameworks, and future directions. Electronics, 14(6):1197, 2025.
Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip Yu, and Lichao Sun. A survey of ai-generated content (aigc). ACM Computing Surveys, 57(5):1–38, 2025.
Alessandro Ansani, Friederike Koehler, Lisa Giombini, Matias Hämäläinen, Chen Meng, Marco Marini, and Suvi Saarikallio. Ai performer bias: Listeners like music less when they think it was performed by an ai. Empirical Studies of the Arts, 1(1):1–25, 2025.
João D’Assumpção, Gilberto Almeida, and Mariza Ferro. Os potenciais impactos Ético legais da aplicação de modelos generativos de Áudio na música. In Anais do V Work shop sobre as Implicações da Computação na Sociedade, pages 80–88, Porto Alegre, RS, Brasil, 2024. SBC.
Patrick Biernacki and Dan Waldorf. Snowball sampling: Problems and techniques of chain referral sampling. Sociological methods & research, 10(2):141–163, 1981.
Francisco Tigre Moura and Charlotte Maw. Artificial intelligence became beethoven: how do listeners and music professionals perceive artificially composed music? Journal of Consumer Marketing, 38(2):137–146, 2021.
Eric Drott. Copyright, compensation, and commons in the music ai industry. Creative Industries Journal, 14(2):190–207, 2021.
Gianluca Micchi, Louis Bigo, Mathieu Giraud, Richard Groult, and Florence Levé. I keep counting: An experiment in human/AI co-creative songwriting. Transactions of the International Society for Music Information Retrieval (TISMIR), 4(1):263–275, 2021.
António Correia. On the human-ai metaphorical interplay for culturally sensitive generative ai design in music cocreation. In ACM IUI Workshops, 2024.
Peter Knees, Markus Schedl, and Masataka Goto. Intelligent user interfaces for music discovery. Transactions of the International Society for Music Information Retrieval, 3(1):165––179, 2020.
Martin Rohrmeier. On creativity, music’s ai completeness, and four challenges for artificial musical creativity. Transactions of the International Society for Music Information Retrieval, 5(1):50–66, 2022.
Kyungyun Lee, Gladys Hitt, Emily Terada, and Jin Ha Lee. Ethics of singing voice synthesis: Perceptions of users and developers. In ISMIR, pages 733–740, 2022.
Dimiter Zlatkov, Jeff Ens, and Philippe Pasquier. Searching for human bias against ai-composed music. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar), pages 308–323. Springer, 2023.
Francesca Ronchini, Luca Comanducci, Gabriele Perego, and Fabio Antonacci. Paguri: a user experience study of creative interaction with text-to-music models. arXiv preprint arXiv:2407.04333, 2024.
Michele Newman, Lidia Morris, and Jin Ha Lee. Human-ai music creation: Understanding the perceptions and experiences of music creators for ethical and productive collaboration. In ISMIR, pages 80–88, 2023.
Renaud Bougueng Tchemeube, Jeffrey Ens, Cale Plut, Philippe Pasquier, Maryam Safi, Yvan Grabit, and Jean-Baptiste Rolland. Evaluating human-ai interaction via usability, user experience and acceptance measures for mmm-c: A creative ai system for music composition. In IJCAI, pages 5769–5778, 2023.
Hyeshin Chu, Joohee Kim, Seongouk Kim, Hongkyu Lim, Hyunwook Lee, Seungmin Jin, Jongeun Lee, Taehwan Kim, and Sungahn Ko. An empirical study on how people perceive ai-generated music. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 304–314, 2022.
Qionglin Li and Yumeng Ma. A study on social perception and acceptance of ai music-taking jj lin’s fans as an example. In 2024 7th International Conference on Humanities Education and Social Sciences (ICHESS 2024), pages 768–779. Atlantis Press, 2024.
Sidarta Prassetyo and Celya Intan Kharisma Putri. Ai in language education: students’ perceptions of creating nursery songs with chatgpt and sunoai. Journal of English in Academic and Professional Communication, 11(1):01–22, 2025.
Francisco Fialho Correia. The role of artificial intelligence in music production: A survey on public acceptance, perception and bias. Master’s thesis, Universidade NOVA de Lisboa (Portugal), 2024.
R. A. Hernández-Nieto. Contributions to Statistical Analysis. Universidad de Los Andes, Mérida, 2002.
Louise Doyle, Anne-Marie Brady, and Gobnait Byrne. An overview of mixed methods research. Journal of research in nursing, 14(2):175–185, 2009.
Juliet M Corbin and Anselm Strauss. Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative sociology, 13(1):3–21, 1990.
Leandre R Fabrigar and Duane T Wegener. Exploratory factor analysis. Oxford University Press, 2012.
Herbert W Marsh, Bengt Muthén, Tihomir Asparouhov, Oliver Lüdtke, Alexander Robitzsch, Alexandre JS Morin, and Ulrich Trautwein. Exploratory structural equation modeling, integrating cfa and efa: Application to students’ evaluations of university teaching. Structural equation modeling: A multidisciplinary journal, 16(3):439–476, 2009.
Hudson Golino and Alexander Christensen. EGAnet: Exploratory Graph Analysis – A framework for estimating the number of dimensions in multivariate data using network psychometrics, 2025. R package version 2.1.1.
Hudson F Golino and Andreas Demetriou. Estimating the dimensionality of intelligence like data using exploratory graph analysis. Intelligence, 62:54–70, 2017.
David Goretzko. How many factors to retain in exploratory factor analysis? a critical overview of factor retention methods. Psychological Methods, 2025.
Larry Wasserman. All of statistics: a concise course in statistical inference. Springer Science & Business Media, 2013.
Geoffrey J McLachlan and Suren Rathnayake. On the number of components in a gaussian mixture model. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(5):341–355, 2014.
Ali Akbar Septiandri, Marios Constantinides, and Daniele Quercia. The potential impact of ai innovations on us occupations. PNAS nexus, 3(9):pgae320, 2024.
Florian Grötschla, Ahmet Solak, Luca A Lanzendörfer, and Roger Wattenhofer. Benchmarking music generation models and metrics via human preference studies. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE, 2025.
Dinh-Viet-Toan Le, Louis Bigo, Dorien Herremans, and Mikaela Keller. Natural language processing methods for symbolic music generation and information retrieval: a survey. ACM Computing Surveys, 57(7):1–40, 2025.
Yujia Zhao, Mingzhi Yang, Yujia Lin, Xiaohong Zhang, Feifei Shi, Zongjie Wang, Jianguo Ding, and Huansheng Ning. Ai-enabled text-to-music generation: A comprehensive review of methods, frameworks, and future directions. Electronics, 14(6):1197, 2025.
Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip Yu, and Lichao Sun. A survey of ai-generated content (aigc). ACM Computing Surveys, 57(5):1–38, 2025.
Alessandro Ansani, Friederike Koehler, Lisa Giombini, Matias Hämäläinen, Chen Meng, Marco Marini, and Suvi Saarikallio. Ai performer bias: Listeners like music less when they think it was performed by an ai. Empirical Studies of the Arts, 1(1):1–25, 2025.
João D’Assumpção, Gilberto Almeida, and Mariza Ferro. Os potenciais impactos Ético legais da aplicação de modelos generativos de Áudio na música. In Anais do V Work shop sobre as Implicações da Computação na Sociedade, pages 80–88, Porto Alegre, RS, Brasil, 2024. SBC.
Patrick Biernacki and Dan Waldorf. Snowball sampling: Problems and techniques of chain referral sampling. Sociological methods & research, 10(2):141–163, 1981.
Francisco Tigre Moura and Charlotte Maw. Artificial intelligence became beethoven: how do listeners and music professionals perceive artificially composed music? Journal of Consumer Marketing, 38(2):137–146, 2021.
Eric Drott. Copyright, compensation, and commons in the music ai industry. Creative Industries Journal, 14(2):190–207, 2021.
Gianluca Micchi, Louis Bigo, Mathieu Giraud, Richard Groult, and Florence Levé. I keep counting: An experiment in human/AI co-creative songwriting. Transactions of the International Society for Music Information Retrieval (TISMIR), 4(1):263–275, 2021.
António Correia. On the human-ai metaphorical interplay for culturally sensitive generative ai design in music cocreation. In ACM IUI Workshops, 2024.
Peter Knees, Markus Schedl, and Masataka Goto. Intelligent user interfaces for music discovery. Transactions of the International Society for Music Information Retrieval, 3(1):165––179, 2020.
Martin Rohrmeier. On creativity, music’s ai completeness, and four challenges for artificial musical creativity. Transactions of the International Society for Music Information Retrieval, 5(1):50–66, 2022.
Kyungyun Lee, Gladys Hitt, Emily Terada, and Jin Ha Lee. Ethics of singing voice synthesis: Perceptions of users and developers. In ISMIR, pages 733–740, 2022.
Dimiter Zlatkov, Jeff Ens, and Philippe Pasquier. Searching for human bias against ai-composed music. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar), pages 308–323. Springer, 2023.
Francesca Ronchini, Luca Comanducci, Gabriele Perego, and Fabio Antonacci. Paguri: a user experience study of creative interaction with text-to-music models. arXiv preprint arXiv:2407.04333, 2024.
Michele Newman, Lidia Morris, and Jin Ha Lee. Human-ai music creation: Understanding the perceptions and experiences of music creators for ethical and productive collaboration. In ISMIR, pages 80–88, 2023.
Renaud Bougueng Tchemeube, Jeffrey Ens, Cale Plut, Philippe Pasquier, Maryam Safi, Yvan Grabit, and Jean-Baptiste Rolland. Evaluating human-ai interaction via usability, user experience and acceptance measures for mmm-c: A creative ai system for music composition. In IJCAI, pages 5769–5778, 2023.
Hyeshin Chu, Joohee Kim, Seongouk Kim, Hongkyu Lim, Hyunwook Lee, Seungmin Jin, Jongeun Lee, Taehwan Kim, and Sungahn Ko. An empirical study on how people perceive ai-generated music. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 304–314, 2022.
Qionglin Li and Yumeng Ma. A study on social perception and acceptance of ai music-taking jj lin’s fans as an example. In 2024 7th International Conference on Humanities Education and Social Sciences (ICHESS 2024), pages 768–779. Atlantis Press, 2024.
Sidarta Prassetyo and Celya Intan Kharisma Putri. Ai in language education: students’ perceptions of creating nursery songs with chatgpt and sunoai. Journal of English in Academic and Professional Communication, 11(1):01–22, 2025.
Francisco Fialho Correia. The role of artificial intelligence in music production: A survey on public acceptance, perception and bias. Master’s thesis, Universidade NOVA de Lisboa (Portugal), 2024.
R. A. Hernández-Nieto. Contributions to Statistical Analysis. Universidad de Los Andes, Mérida, 2002.
Louise Doyle, Anne-Marie Brady, and Gobnait Byrne. An overview of mixed methods research. Journal of research in nursing, 14(2):175–185, 2009.
Juliet M Corbin and Anselm Strauss. Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative sociology, 13(1):3–21, 1990.
Leandre R Fabrigar and Duane T Wegener. Exploratory factor analysis. Oxford University Press, 2012.
Herbert W Marsh, Bengt Muthén, Tihomir Asparouhov, Oliver Lüdtke, Alexander Robitzsch, Alexandre JS Morin, and Ulrich Trautwein. Exploratory structural equation modeling, integrating cfa and efa: Application to students’ evaluations of university teaching. Structural equation modeling: A multidisciplinary journal, 16(3):439–476, 2009.
Hudson Golino and Alexander Christensen. EGAnet: Exploratory Graph Analysis – A framework for estimating the number of dimensions in multivariate data using network psychometrics, 2025. R package version 2.1.1.
Hudson F Golino and Andreas Demetriou. Estimating the dimensionality of intelligence like data using exploratory graph analysis. Intelligence, 62:54–70, 2017.
David Goretzko. How many factors to retain in exploratory factor analysis? a critical overview of factor retention methods. Psychological Methods, 2025.
Larry Wasserman. All of statistics: a concise course in statistical inference. Springer Science & Business Media, 2013.
Geoffrey J McLachlan and Suren Rathnayake. On the number of components in a gaussian mixture model. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(5):341–355, 2014.
Publicado
15/09/2025
Como Citar
PEDROSA, Frederico; FIGUEIREDO, Flávio; MACHADO, Alexei; MORIÁ, Ivan; FERREIRA, Lucas.
Dimensions on the Perception of Users Towards AI Generated Music. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 19. , 2025, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 44-51.
DOI: https://doi.org/10.5753/sbcm.2025.14454.
