When I find interesting related articles and books I try and and find time to pop it here, lit review style. A work in progress.
Agres, K., & Herremans, D. (2020). Music and Artificial Intelligence: From Composition to Performance. Frontiers in Artificial Intelligence.
Nice overview AI-supported creative systems. Give a big picture view of how these types of systems integrate with ML, improvisation, and performance research.
Bown, O., & Martin, A. (2019). Machine Listening: Improvisation with Algorithms. Organised Sound.
Cool article - explores algorithmic improvisers as co-creative agents, offering concrete design considerations for making systems “feel musical.” Emphasizes evaluation, responsiveness, and human-centred interaction—all central to Deep Listening.
Fiebrink, R. (2011). Real-Time Human Interaction with Supervised Learning Algorithms for Music Composition and Performance. Princeton University.
Really thorough and helpful. Rebecca Fiebrink’s thesis is a great starting place for the integration of machine learning for musicians. Lots of detail here talking about how performers can actively train, refine, and reshape ML models during performance, enabling adaptive mappings and personalised behaviours. There are some really novel approaches in this text in relation to performance.
Lartillot, O., Toiviainen, P., & Eerola, T. (2008). A MATLAB Toolbox for Music Information Retrieval. Data Analysis, Machine Learning and Applications.
Not so much a real-time thing, but a good way to get across the language of audio information and how it can be used as features (explaining concepts such as onsets, chroma, spectral flux, centroids etc.)
Lewis, G. E. (2000). Too Many Notes: Computers, Complexity, and Culture in Voyager. Leonardo Music Journal.
Examines the the Voyager, kind of a historical prototype for fully autonomous improvising agents. Lewis’s framing—non-hierarchical human–machine interplay, distributed agency, and computational “musical personalities”—is a cool read for thinking about the kind of aesthetic questions that always pop when designing these systems.
Oliveros, P. (2005). Deep Listening: A Composer’s Sound Practice. iUniverse.
This is an interesting foundational philosophical work, presenting listening as an active, relational, and ethical practice rather than passive activity. There is really intresting emphasis in this writing on attentiveness, reciprocity and co-presence in music. Think of it as a philislophical foundation to undeping how technology might be designed to respond to humans as creative partners.
Wessel, D., & Wright, M. (2002). Problems and Prospects for Intimate Musical Control of Computers. Computer Music Journal.
Raises some interesting issues around latency, gesture mapping, and multimodal control — topics central to designing responsive, expressive live systems, and also using a combination or protocols to assist improvisation.
Wright, M., & Freed, A. (1997). Open Sound Control: A New Protocol for Communicating with Sound Synthesizers. ICMC.
Older article - but just raises this issue around issues with MIDI as a protocol. Outlines OSC’s advantages over MIDI: higher resolution, network friendliness, address hierarchies, and time-tagged bundles. These are essential in your setup where MIDI controllers, a Raspberry Pi, SuperCollider, and Python ML models communicate in real time.