The paper frames unconducted vocal ensemble singing as coupled dynamic systems without an external reference and proposes an architecture for an AI singer that enters, rather than tracks, the ensemble's collective state.
Musical Agent Systems: MACAT and MACataRT
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abstract
Our research explores the development and application of musical agents, human-in-the-loop generative AI systems designed to support music performance and improvisation within co-creative spaces. We introduce MACAT and MACataRT, two distinct musical agent systems crafted to enhance interactive music-making between human musicians and AI. MACAT is optimized for agent-led performance, employing real-time synthesis and self-listening to shape its output autonomously, while MACataRT provides a flexible environment for collaborative improvisation through audio mosaicing and sequence-based learning. Both systems emphasize training on personalized, small datasets, fostering ethical and transparent AI engagement that respects artistic integrity. This research highlights how interactive, artist-centred generative AI can expand creative possibilities, empowering musicians to explore new forms of artistic expression in real-time, performance-driven and music improvisation contexts.
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2026 1verdicts
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Beyond Call and Response: Modelling Reciprocal Coordination in Human-AI Vocal Ensembles
The paper frames unconducted vocal ensemble singing as coupled dynamic systems without an external reference and proposes an architecture for an AI singer that enters, rather than tracks, the ensemble's collective state.