Recurring patterns: An ethnographic study on the adoption of AI music tools by practitioners of electroacoustic, contemporary and popular musics | Intellect Skip to content
1981
  1. Home
  2. A-Z Publications
  3. Journal of Pervasive Media
  4. Volume 8, Issue 1
  5. Article
AI, Augmentation and Art
  • ISSN: 2633-8793
  • E-ISSN: 2633-8785

Abstract

The intersection of artificial intelligence (AI) and art has been a topic of great interest in recent times. Driven by greater visibility of accessible AI applications within mainstream media, artists have increased their uptake of such tools as means of exploring and expanding their creative expressions. With the music industry also displaying similar levels of curiosity for AI tools, practitioners and audiences voice diverging opinions on the topics of artistic authenticity, creative labour and the threats posed by thinking machines on the future of musicians’ careers. This article aims to explore these topics through an ethnographic study conducted through interviews with five composers active in the areas of electroacoustic music, contemporary composition and experimental electronic music. The discussions reveal some of the software and methodologies currently popular among composers, the challenges faced and avenues presented when adopting AI tools, as well as the attitudes and discourse that permeate the niche circles of AI-generated music. The findings point towards the swift uptake of new technologies by curious artists and the slow development of trust in AI applications by traditionalist makers and listeners, suggesting a continuation of the patterns of behaviour evident since the emergence of music technology.

© 2023 Intellect Ltd
Loading

Article metrics loading...

/content/journals/10.1386/jpm_00004_1
2023-08-18
2024-04-27
Loading full text...

Full text loading...

References

  1. Alloghani, M.,, Al-Jumeily, D.,, Mustafina, J.,, Hussain, A., and Aljaaf, A. J.. ( 2020;), ‘ A systematic review on supervised and unsupervised machine learning algorithms for data science. ’, in M. Berry,, A. Mohamed, and B. Yap. (eds), Supervised and Unsupervised Learning for Data Science. Unsupervised and Semi-Supervised Learning, Cham:: Springer;, pp. 3–21, https://doi.org/10.1007/978-3-030-22475-2_1.
     [Google Scholar]
  2. Asonitis, T.. ( 2023;), personal interview, online, February..
  3. Asonitis, T.,, Allmendinger, R.,, Benatan, M., and Climent, R.. ( 2022;), ‘ SonOpt: Sonifying bi-objective population-based optimization algorithms. ’, in T. Martins,, N. Rodríguez-Fernández, and S. M. Rebelo. (eds), Artificial Intelligence in Music, Sound, Art and Design: EvoMUSART 2022. Lecture Notes in Computer Science, vol. 13221, Cham:: Springer;, pp. 3–18, https://doi.org/10.1007/978-3-031-03789-4_1.
     [Google Scholar]
  4. Broadway, D.. ( 2023;), ‘ New Grammy Award rules require human input, curb artificial intelligence use. ’, Reuters , 16 June, https://www.reuters.com/lifestyle/grammys-bans-ai-only-music-allows-only-human-creators-2023-06-16/. Accessed 20 June 2023.
  5. Bruce-Jones, H.. ( 2018;), ‘ Listen to Actress’s mini-album with A.I. sprite Young Paint. ’, Fact Magazine, 28 September, https://www.factmag.com/2018/09/28/listen-actress-young-paint-mini-album/. Accessed 12 June 2022.
     [Google Scholar]
  6. Bulayenko, O.,, Quintais, J. P.,, Poort, J., and Gervais, D.. ( 2022;), ‘ AI music outputs: Challenges to the copyright legal framework: Part I. ’, Kluwer Copyright Blog , 22 April, https://copyrightblog.kluweriplaw.com/2022/04/22/ai-music-outputs-challenges-to-the-copyright-legal-framework-part-i/. Accessed 2 February 2023.
  7. Caillon, A., and Esling, E.. ( 2021;), ‘ RAVE: A variational autoencoder for fast and high-quality neural audio synthesis. ’, arXiv:2111.05011v2, https://doi.org/10.48550/arXiv.2111.05011.
  8. Carre, B.. ( 2018), Hello World, LP, SKYGGE, France:: Flow Records;.
     [Google Scholar]
  9. Cave, N.. ( 2023;), ‘ ChatGPT, what do you think?. ’, The Red Hand Files , 16 January, https://www.theredhandfiles.com/chat-gpt-what-do-you-think/. Accessed 22 January 2023.
  10. Civit, M.,, Civit-Masot, J.,, Cuadrado, F., and Escalona, M. J.. ( 2022;), ‘ A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends. ’, Expert Systems with Applications, 209, 118190, https://doi.org/10.1016/j.eswa.2022.118190.
     [Google Scholar]
  11. Clancy, M.. ( 2021;), ‘ Reflections on the financial and ethical implications of music generated by artificial intelligence. ’, Ph.D. thesis, Dublin:: School of Creative Arts, Trinity College;.
     [Google Scholar]
  12. Clancy, M.. ( 2023), Artificial Intelligence and Music Ecosystem, New York and Abingdon:: Routledge;.
     [Google Scholar]
  13. Faqir-Rhazoui, Y.,, Arroyo, J., and Hassan, S.. ( 2021;), ‘ A comparative analysis of the platforms for decentralized autonomous organizations in the Ethereum blockchain. ’, Journal of Internet Services and Applications, 12:9, n.pag., https://doi.org/10.1186/s13174-021-00139-6.
     [Google Scholar]
  14. Finlayson, A.. ( 2023), ‘ AI and music-making part 1: The state of play’, Ableton;, 30 May, https://www.ableton.com/en/blog/ai-and-music-making-the-state-of-play/. Accessed 12 June 2023.
     [Google Scholar]
  15. Forde, E.. ( 2023;), ‘ The music industry frets about AI music: But is it just a new form of muzak?. ’, Music Business Worldwide , 31 January, https://www.musicbusinessworldwide.com/the-music-industry-frets-about-ai-music-but-is-it-just-a-new-form-of-muzak12/. Accessed 20 January 2023.
  16. Hagan, K. L.. ( 2005;), ‘ Genetic analysis of analogique B. ’, in Proceedings of the 2005 Electroacoustic Music Studies Network, Montreal, October, http://www.ems-network.org/spip.php?article150. Accessed 8 July 2023.
     [Google Scholar]
  17. Hellström, T., and Bensch, S.. ( 2022;), ‘ Apocalypse now: No need for artificial general intelligence. ’, AI & Society, n.pag., https://doi.org/10.1007/s00146-022-01526-8.
     [Google Scholar]
  18. Herdon, H.. ( 2019), Proto, LP, UK:: 4AD;.
     [Google Scholar]
  19. Hu, C.. ( 2019;), ‘ Episode 10 (ft. Alex Mitchell): How artificial intelligence will do to music creation what Instagram did to photography. ’, Apple Podcasts , apple.com/us/podcast/episode-10-ft-alex-mitchell-how-artificial-intelligence/id1454221845?i=1000445574177. Accessed 1 June 2022.
  20. Kamalgharan, I.. ( 2023), personal interview, online, June.
  21. Kersting, K.. ( 2018;), ‘ Machine learning and artificial intelligence: Two fellow travelers on the quest for intelligent behavior in machines. ’, Frontiers in Big Data, 1:6, n.pag., https://doi.org/10.3389/fdata.2018.00006.
     [Google Scholar]
  22. Kim, R.. ( 2023), personal interview, online, May.
  23. Knotts, S., and Collins, N.. ( 2020;), ‘ A survey on the uptake of music AI software. ’, in Proceedings of the International Conference on New Interfaces for Musical Expression (NIME), Birmingham, pp. 499–504, https://doi.org/10.5281/zenodo.4813499.
     [Google Scholar]
  24. Knotts, S., and Collins, N.. ( 2021;), ‘ AI-Lectronica: Music AI in clubs and studio production. ’, in E. R. Miranda. (ed.), Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity, Cham:: Springer;, pp. 849–71.
     [Google Scholar]
  25. Ma, B.. ( 2023), personal interview, online, January.
  26. Miranda, E. R.. ( 2021), Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity, Cham:: Springer;.
     [Google Scholar]
  27. Morreale, F.. ( 2021;), ‘ Where does the buck stop? Ethical and political issues with AI in music creation. ’, Transactions of the International Society for Music Information Retrieval, 4:1, pp. 105–12, https://doi.org/10.5334/tismir.86.
     [Google Scholar]
  28. Nercessian, S.. ( 2018;), ‘ iZotope and assistive audio technology. ’, iZotope , 19 July, www.izotope.com/en/learn/izotope-and-assistive-audio-technology.html. Accessed 10 January 2023.
  29. Roberts, T. J. H.. ( 2023), personal interview, online, February.
  30. Salmi, J.. ( 2022;), ‘ A democratic way of controlling artificial general intelligence. ’, AI & Society, 38:4, pp. 1785–91, https://doi.org/10.1007/s00146-022-01426-x.
     [Google Scholar]
  31. Sandred, Ö.,, Laurson, M., and Kuuskankare, M.. ( 2009;), ‘ Revisiting the Illiac Suite: A rule-based approach to stochastic processes. ’, Sonic Ideas/Ideas Sónicas, 1:2, pp. 42–46.
     [Google Scholar]
  32. Sarker, I. H.. ( 2021;), ‘ Machine learning: Algorithms, real-world applications and research directions. ’, SN Computer Science, 2:160, https://doi.org/10.1007/s42979-021-00592-x.
     [Google Scholar]
  33. Sturm, B. L. T.,, Iglesias, M.,, Ben-Tal, O.,, Miron, M., and Gómez, E.. ( 2019;), ‘ Artificial intelligence and music: Open questions of copyright law and engineering praxis. ’, Arts, 8:3, pp. 1–15, https://www.mdpi.com/2076-0752/8/3/115/htm#B39-arts-08-00115. Accessed 1 June 2022.
     [Google Scholar]
  34. UnSupervised ( 2022;), ‘ A music festival exploring AI, ML and all things in-between. ’, UnSupervised , 27–28 June, https://www.unsupervised.uk/. Accessed 29 June 2022.
http://instance.metastore.ingenta.com/content/journals/10.1386/jpm_00004_1
Loading
Recurring patterns: An ethnographic study on the adoption of AI music tools by practitioners of electroacoustic, contemporary and popular musics
Journal of Pervasive Media 8, 51 (2023); https://doi.org/10.1386/jpm_00004_1
/content/journals/10.1386/jpm_00004_1
/content/journals/10.1386/jpm_00004_1
Loading

Data & Media loading...

  • Article Type: Article
Keyword(s): artificial intelligence; culture; ethnography; interview; music; music composition
This is a required field
Please enter a valid email address
Approval was a success
Invalid data
An error occurred
Approval was partially successful, following selected items could not be processed due to error