In Betweenness

In Betweenness is an artistic research project exploring the evolving relationship between human performers and AI agents through embodied interaction, sound synthesis, and immersive audiovisual systems. The work integrates concatenative corpus-based machine learning with a neural audio model (RAVE) trained on a hybrid dataset of synthesized, acoustic, and AI-generated instrumental sounds, creating a dynamic sonic environment in which human intention and algorithmic interpretation continually interact.

Embodied gesture functions as both input and expressive medium: the performer’s movements are captured and translated in real time into sound and visuals, shaping the system’s behaviour. Spatialized using Higher-Order Ambisonics (HOA) and visualized through TouchDesigner, the work forms an integrated audiovisual feedback loop that can adapt across playback systems.

An AI agent continuously analyzes the performer’s gestures and responds through adaptive sonic and visual transformations, creating a reciprocal relationship that redistributes agency between human and machine. Positioned within contemporary discourse on Human–AI collaboration, In Betweenness proposes a performative space where meaning emerges through interaction, inviting reflection on presence, authorship, and communication in mediated environments.