Computer-Supported Musical Improvisation

My doctoral research asks how technology can support musical improvisation, the social musical play practiced across cultures throughout human history. It identifies technology’s role in improvisation, the investigations well positioned to support it, and builds and evaluates systems that push the boundaries of technology and improvisation.

Framework

I organize the work around four areas that support improvisation and the communication of musical ideas between players: sensing, processing, representing, and networking musical information.

Four parts of the dissertation

  • Expanding the circle of musicians: colored notation, Jam Tabs, and the Jam Station.
  • Jamming at a distance: AR Drum Circle, network latency perception, limited field of view with holograms, and XR Jam.
  • Jamming with computers: personified intelligent music tools, networked AI-driven virtual musicians, Virtual AI Jam, and BrAIn Jam.
  • Incorporating the audience: Rhythmic Conversations and Stringesthesia.

Publications

  1. Computer-Supported Musical Improvisation

    Torin Hopkins

    New Interfaces for Musical Expression (NIME), Doctoral Consortium, Mexico City, Mexico, 2023.

  2. Computer-Supported Music Improvisation

    Torin Hopkins

    Ph.D. Dissertation, University of Colorado Boulder (ProQuest), Boulder, CO, USA, 2024.

  3. Augmented Personification of Intelligent Music Tools for Creativity and Collaboration

    Torin Hopkins, Rishi Vanukuru, Suibi Che-Chuan Weng, Amy Banić, and Ellen Yi-Luen Do

    ACM CHI Conference on Human Factors in Computing Systems (Workshop), New Orleans, LA, USA, 2022.

TENOR 2020

Jam Tabs

Investigating alternative, color-based music notation and tools for novice improvisation.

Frontiers in Virtual Reality · 2022

AR Drum Circle

Networked musical interaction and real-time collaboration between remote drummers in augmented reality.

Frontiers in Computer Science · 2025

BrAIn Jam

Controlling an AI-driven virtual drummer using neural signals (fNIRS) in real time, with a comparison of machine learning models for interpreting neural data.

Audio Mostly 2023

Stringesthesia

Dynamically shifting musical agency between audience and performer based on the performer’s neurally measured trust in an improvised performance.