Data Knot: Machine Learning for low latency real-time performance

Rodrigo Constanzo (Royal Northern College of Music)*; Jordie Shier (Centre for Digital Music Queen Mary University of London)

Data Knot: Machine Learning for low latency real-time performance
Image credit: Rodrigo Constanzo (Royal Northern College of Music)*; Jordie Shier (Centre for Digital Music Queen Mary University of London)
  • The registration for this workshop is managed by the workshop chairs. A sign-up form will be circulated in late May
  • Type: Hybrid
  • Room Location: 103
  • Morning (9am-1pm)
  • Signup Deadline: June 19th AoE (first-come first-served)
  • Contact Email: here

Abstract:

This workshop centers around Data Knot, a machine learning package designed for low-latency, real-time musical performance within the FluCoMa ecosystem. Addressing the gap between existing ML tools and the needs of music practitioners, the workshop frames machine learning as an accessible, creative material rather than a specialized technical domain. Through a combination of guided lectures, hands-on coding, and group discussion, participants will learn to build classifiers and regressors, explore descriptor-based audio analysis, and develop personalized performance patches in Max. Emphasizing experimentation, artistic context, and community exchange, the workshop aims to lower barriers to ML adoption while fostering meaningful engagement with diverse music-making practices.