mlsanityData to 7 Oct 2026

Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Design

arXiv 2502.09055 ↗First posted 13 Feb 2025cs.HC cs.AIcited by 3

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Abstract (arXiv)

Recent advances in generative AI music have resulted in new technologies that are being framed as co-creative tools for musicians with early work demonstrating their potential to add to music practice. While the field has seen many valuable contributions, work that involves practising musicians in the design and development of these tools is limited, with the majority of work including them only once a tool has been developed. In this paper, we present a case study that explores the needs of practising musicians through the co-design of a musical variation system, highlighting the importance of involving a diverse range of musicians throughout the design process and uncovering various design insights. This was achieved through two workshops and a two week ecological evaluation, where musicians from different musical backgrounds offered valuable insights not only on a musical system's design but also on how a musical AI could be integrated into their musical practices.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 76 references that are in the map. First posted on arXiv 13 Feb 2025.

Builds on

19 of 76 references.

References · 19

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023MusicLM: Generating Music From Textcited by 442
  3. 2018Music Transformercited by 207
  4. 2021Symbolic Music Generation with Diffusion Modelscited by 152
  5. 2018A Hierarchical Latent Vector Model for Learning Long-Term Structure in Musiccited by 131
  6. 2021MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Trainingcited by 89
  7. 2022Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systemscited by 59
  8. 2020MMM : Exploring Conditional Multi-Track Music Generation with the Transformercited by 48
  9. 2021MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAEcited by 39
  10. 2019Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creatorscited by 31
  11. 2020AI Song Contest: Human-AI Co-Creation in Songwritingcited by 25
  12. 2019Learning to Traverse Latent Spaces for Musical Score Inpaintingcited by 24
7 more
  1. 2024Understanding Nonlinear Collaboration between Human and AI Agents: A Co-design Framework for Creative Designcited by 22
  2. 2023ReelFramer: Human-AI Co-Creation for News-to-Video Translationcited by 19
  3. 2021Nine Potential Pitfalls when Designing Human-AI Co-Creative Systemscited by 12
  4. 2023Understanding Visual Arts Experiences of Blind Peoplecited by 9
  5. 2024Towards A Diffractive Analysis of Prompt-Based Generative AIcited by 7
  6. 2023Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AIcited by 4
  7. 2016Remixing as a Pathway to Computational Thinkingcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Opening Musical Creativity? Embedded Ideologies in Generative-AI Music Systemscited by 4
  2. 2026Live Music Diffusion Models: Efficient Fine-Tuning and Post-Training of Interactive Diffusion Music Generatorscited by 1
  3. 2025Supporting Creative Ownership through Deep Learning-Based Music Variationcited by 0