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arxiv: 2605.29931 · v1 · pith:E4PJWGYNnew · submitted 2026-05-28 · 💻 cs.AI · eess.AS

It`s All About Speed: AI`s Impact on Workflow in Music Production

Finn McClellan , Fabio Morreale This is my paper

Pith reviewed 2026-06-29 07:40 UTC · model grok-4.3

classification 💻 cs.AI eess.AS
keywords AI toolsmusic productionworkflowethnographic studycreative agencytool designprofessional users
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The pith

AI tools in music production create tensions around speed, controllability, and creative agency that better design can resolve.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper presents findings from an ethnographic study with professional recording engineers, mixers, and producers on how AI and automated tools affect their workflows. It highlights that while these tools offer efficiency gains, they generate tensions in the need for speed, maintaining control, and preserving creative agency. The authors suggest that these tensions can be reduced through thoughtful tool design that aligns with user needs. Readers should care because it provides insight into real user experiences with AI in a creative field rather than abstract benefits.

Core claim

The paper establishes through interviews that AI tools impact music production by creating tensions between users and automation in key areas including the need for speed and efficiency, controllability, and maintaining creative agency, and that these tensions may be alleviated through tool design.

What carries the argument

Ethnographic interviews revealing user sentiments and tensions with AI tools in professional music workflows, with design recommendations to address them.

If this is right

  • Users value speed but not at the expense of control over the process.
  • Creative agency must be preserved for professional satisfaction.
  • Tool designers should focus on alleviating specific tensions in controllability and agency.
  • Proliferation of AI tools will continue to shape professional practices in music production.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar tensions may exist in other creative industries adopting AI, such as visual arts or writing.
  • Future studies could test these findings with quantitative surveys across more participants.
  • Design principles identified could be applied to develop specific AI features for music software.

Load-bearing premise

The views collected from the selected professional participants accurately represent the experiences and tensions present across the wider population of music production professionals using AI tools.

What would settle it

A study with a larger and more diverse group of music production professionals that finds no significant tensions in speed, controllability, or creative agency would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.29931 by Fabio Morreale, Finn McClellan.

Figure 2
Figure 2. Figure 2: Soothe2 Ozone Ozone is an intelligent mastering tool made by Izo￾tope.9 It listens and applies a track-specific mastering chain with various user controls [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Ozone XO XO is a drum sampler made by XLN Audio10 that uses AI to automatically categorise and map drum samples, allowing the user to search based on sonic similarities and providing a more musical way to categorise files [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 1
Figure 1. Figure 1: The God Particle Soothe2 Soothe2 is a semi-automated dynamic resonance sup￾pressor manufactured by Oeksound.8 The plugin pro￾vides users a range of controls for which frequency range to focus on, and how aggressive and sensitive the filtering should be. 7https://cradle.app/products/ the-god-particle 8https://oeksound.com/plugins/soothe2/ [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 4
Figure 4. Figure 4: XO’s sample map 9https://www.izotope.com/en/products/ozone. html 10https://www.xlnaudio.com/products/xo AES International Conference on Machine Learning and Artificial Intelligence for Audio, London, UK 2025 September 8–10 Page 10 of 10 [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
read the original abstract

In this paper, we present the results of an ethnographic study into the impact of AI and automated tools on music production workflow. Focusing specifically on professional participants who identified as recording engineers, mixers, and producers, we discuss their usage of common AI and automated software, as well as their sentiments on the proliferation of these tools. We discuss tensions that may be created between users and automated tools in key areas such as the need for speed and efficiency, controllability, and maintaining creative agency, and how these tensions may be alleviated through tool design.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper reports results from an ethnographic study of professional recording engineers, mixers, and producers on their use of AI and automated tools in music production workflows. It identifies tensions arising in areas of speed/efficiency, controllability, and creative agency, and argues that these can be alleviated through improved tool design.

Significance. If the empirical claims are robustly supported, the work could inform human-AI collaboration research in creative domains and guide more usable AI tool development for music production. The qualitative focus on professional users is a strength, but the absence of methodological transparency limits the ability to assess generalizability or practical impact.

major comments (2)
  1. [Abstract/Methods] Abstract and Methods: No details are supplied on participant recruitment, sample size, interview protocol, geographic or career-stage diversity, or analysis method. Without this information it is not possible to determine whether the reported tensions are supported by the data or can support design recommendations for the wider population.
  2. [Discussion] The central claim that tensions 'may be alleviated through tool design' is presented without concrete links to participant statements or specific design examples drawn from the study, making the practical implications difficult to evaluate.
minor comments (1)
  1. [Title] Title contains a typographical error ('It`s' instead of 'It's').

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight opportunities to strengthen the manuscript's methodological transparency and the grounding of its design implications. We address each major comment below and will revise the paper to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: [Abstract/Methods] Abstract and Methods: No details are supplied on participant recruitment, sample size, interview protocol, geographic or career-stage diversity, or analysis method. Without this information it is not possible to determine whether the reported tensions are supported by the data or can support design recommendations for the wider population.

    Authors: We agree that the current Methods section lacks sufficient detail for assessing the study's scope and rigor. The revised manuscript will expand this section to describe participant recruitment (via industry networks and events), sample size, interview protocol, participant diversity across geography and career stages, and the thematic analysis method employed. This addition will directly address the concern about evaluating the reported tensions and their broader applicability. revision: yes

  2. Referee: [Discussion] The central claim that tensions 'may be alleviated through tool design' is presented without concrete links to participant statements or specific design examples drawn from the study, making the practical implications difficult to evaluate.

    Authors: We concur that the Discussion would be strengthened by more explicit ties to the empirical data. The revision will add direct participant quotes illustrating the tensions and propose specific design examples (such as enhanced parameter controls or agency-preserving interfaces) drawn from the study findings to make the practical implications clearer and more actionable. revision: yes

Circularity Check

0 steps flagged

No circularity: qualitative ethnographic study with independent empirical basis

full rationale

The paper reports results from an ethnographic study of professional music producers, engineers, and mixers, deriving claims about workflow tensions directly from participant data and observations. No equations, parameters, derivations, or fitted inputs exist. Claims rest on external interview evidence rather than reducing to self-definition, self-citation chains, or renamed inputs. The study is self-contained against its own data collection; generalizability concerns are separate from circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the validity and representativeness of qualitative data from an ethnographic study. No free parameters, mathematical axioms, or invented entities are present in the abstract.

pith-pipeline@v0.9.1-grok · 5612 in / 1034 out tokens · 33041 ms · 2026-06-29T07:40:54.897570+00:00 · methodology

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Reference graph

Works this paper leans on

23 extracted references · 2 canonical work pages

  1. [1]

    The role of failure in developing creativity in professional music recording and pro- duction,

    Thorley, M., “The role of failure in developing creativity in professional music recording and pro- duction,”Thinking Skills and Creativity, 30, pp. 160–170, 2018

    2018

  2. [2]

    Adoption of AI Technology in Mu- sic Mixing Workflow: An Investigation,

    Sai Vanka, S., Safi, M., Rolland, J.-B., and Fazekas, G., “Adoption of AI Technology in Mu- sic Mixing Workflow: An Investigation,” inAudio Engineering Society Convention 154, Audio En- gineering Society, 2023

    2023

  3. [3]

    The rise of the remote mix engineer: Technology, expertise, star,

    Thorley, M., “The rise of the remote mix engineer: Technology, expertise, star,”Creative Industries Journal, 12(3), pp. 301–313, 2019

    2019

  4. [4]

    Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production,

    Fu, Y ., Newman, M., Going, L., Feng, Q., and Lee, J. H., “Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production,”arXiv preprint arXiv:2501.15276, 2025

    work page arXiv 2025

  5. [5]

    Beyond intelligent machines: just do it,

    Shneiderman, B., “Beyond intelligent machines: just do it,”IEEE software, 10(1), pp. 100–103, 1993

    1993

  6. [6]

    Automation accuracy is good, but high controllability may be better,

    Roy, Q., Zhang, F., and V ogel, D., “Automation accuracy is good, but high controllability may be better,” inProceedings of the 2019 CHI Confer- ence on Human Factors in Computing Systems, pp. 1–8, 2019

    2019

  7. [7]

    Should music interaction be easy?

    McDermott, J., Gifford, T., Bouwer, A., and Wagy, M., “Should music interaction be easy?” Music and human-computer interaction, pp. 29– 47, 2013

    2013

  8. [8]

    AI Music Mixing Systems,

    Moffat, D., “AI Music Mixing Systems,”Hand- book of Artificial Intelligence for Music: Founda- tions, Advanced Approaches, and Developments for Creativity, pp. 345–375, 2021

    2021

  9. [9]

    Izhaki, R.,Mixing audio: concepts, practices, and tools, Routledge, 2017

    2017

  10. [10]

    Intelligent audio production strategies informed by best prac- tices,

    Pestana, P. D., Reiss, J. D., et al., “Intelligent audio production strategies informed by best prac- tices,” 2014

    2014

  11. [11]

    Automatic multitrack mixing with a differen- tiable mixing console of neural audio effects,

    Steinmetz, C. J., Pons, J., Pascual, S., and Serra, J., “Automatic multitrack mixing with a differen- tiable mixing console of neural audio effects,” in ICASSP 2021-2021 IEEE International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP), pp. 71–75, IEEE, 2021

    2021

  12. [12]

    Music mix- ing style transfer: A contrastive learning approach to disentangle audio effects,

    Koo, J., Martínez-Ramírez, M. A., Liao, W.-H., Uhlich, S., Lee, K., and Mitsufuji, Y ., “Music mix- ing style transfer: A contrastive learning approach to disentangle audio effects,” inICASSP 2023- 2023 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP), pp. 1–5, IEEE, 2023

    2023

  13. [13]

    A real-time semiautonomous audio panning system for mu- sic mixing,

    Perez_Gonzalez, E. and Reiss, J., “A real-time semiautonomous audio panning system for mu- sic mixing,”EURASIP Journal on Advances in Signal Processing, 2010, pp. 1–10, 2010

    2010

  14. [14]

    Deep learning for auto- matic mixing,

    Steinmetz, C. J., Vanka, S., Martínez-Ramírez, M., and Bromham, G., “Deep learning for auto- matic mixing,”ISMIR, Dec, 2022

    2022

  15. [15]

    Emerging paradigms in music technology: valuing mistakes, glitches and uncer- tainty in the age of generative AI and automation,

    Loor Paredes, M., “Emerging paradigms in music technology: valuing mistakes, glitches and uncer- tainty in the age of generative AI and automation,” AI & SOCIETY, pp. 1–12, 2025

    2025

  16. [16]

    Designing for appropriation,

    Dix, A., “Designing for appropriation,” inPro- ceedings of HCI 2007 The 21st British HCI Group Annual Conference University of Lancaster, UK, BCS Learning & Development, 2007

    2007

  17. [17]

    Place-making: A phenomenological theory of technology appro- priation,

    Riemer, K. and Johnston, R. B., “Place-making: A phenomenological theory of technology appro- priation,” 2012

    2012

  18. [18]

    Hackable instru- ments: supporting appropriation and modification in digital musical interaction,

    Zappi, V . and McPherson, A., “Hackable instru- ments: supporting appropriation and modification in digital musical interaction,”Frontiers in ICT, 5, p. 26, 2018

    2018

  19. [19]

    Interaction and mu- sic technology,

    Fels, S. and Lyons, M., “Interaction and mu- sic technology,” inHuman-Computer Interaction– INTERACT 2011: 13th IFIP TC 13 International Conference, Lisbon, Portugal, September 5-9, 2011, Proceedings, Part IV 13, pp. 691–692, Springer, 2011

    2011

  20. [20]

    Rosso, B., “Creativity and constraint: Exploring the role of constraint in the creative processes of new product and technology development teams AES International Conference on Machine Learning and Artificial Intelligence for Audio, London, UK 2025 September 8–10 Page 9 of 10 McClellan and Morreale It‘s All About Speed: AI‘s Impact on Workflow in Music Pro...

    2025

  21. [21]

    Cover story: Hu- manistic HCI,

    Bardzell, J. and Bardzell, S., “Cover story: Hu- manistic HCI,”interactions, 23(2), pp. 20–29, 2016, ISSN 10725520, doi:10.1145/2888576

    work page doi:10.1145/2888576 2016

  22. [22]

    Ethnography and popular music stud- ies,

    Cohen, S., “Ethnography and popular music stud- ies,”Popular music, 12(2), pp. 123–138, 1993

    1993

  23. [23]

    Thematic analysis,

    Braun, V ., Clarke, V ., Hayfield, N., and Terry, G., “Thematic analysis,” inHandbook of research methods in health social sciences, pp. 843–860, Springer, 2019. Appendix The God Particle The God Particle is an intelligent mix bus processor de- veloped by Cradle in collaboration with award-winning mix engineer Jaycen Joshua.7 Its processing includes EQ, mu...

    2019