Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Design
Stephen James Krol, Maria Teresa Llano Rodriguez, Miguel Loor Paredes
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers1
Ask how each one uses itBuilds on13
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
- Simple and Controllable Music GenerationJade Copet, Felix Kreuk, Itai Gat, Tal Remez et al.NeurIPS 2023 · 843 citations
- Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative ModelsRyan Louie, Andy Coenen, Cheng Zhi Huang, Michael Terry et al.CHI 2020 · 265 citations
- AI as Social Glue: Uncovering the Roles of Deep Generative AI during Social Music CompositionMinhyang (Mia) Suh, Emily Youngblom, Michael Terry, Carrie J. CaiCHI 2021 · 132 citations
Related papers
- Understanding Human-AI Collaboration in Music Therapy Through Co-Design with TherapistsJingjing Sun, Jingyi Yang, Guyue Zhou, Yucheng Jin et al.CHI 2024 · 31 citations
- Sound Designer-Generative AI Interactions: Towards Designing Creative Support Tools for Professional Sound DesignersPurnima Kamath, Fabio Morreale, Priambudi Lintang Bagaskara, Yize Wei et al.CHI 2024 · 36 citations
- From Blank Box to Creative Partner: Designing Ecological On-Ramps for First-Time AI ArtistsCharlotte Bird, Caterina Moruzzi, Ewa LugerCHI 2026 · 1 citation
- Understanding the Potentials and Limitations of Prompt-based Music Generative AIYoujin Choi, JaeYoung Moon, Jinyoung Yoo, Jin-Hyuk HongCHI 2025 · 7 citations
- Understanding Collaboration between Professional Designers and Decision-making AI: A Case Study in the WorkplaceNami Ogawa, Yuki Okafuji, Yuji Hatada, Jun BabaCSCW 2025 · 3 citations
