Musical Agent Systems: MACAT and MACataRT

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.

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Sound (cs.SD); Audio and Speech Processing (eess.AS)

arXiv:2502.00023 [cs.MA]

(or arXiv:2502.00023v2 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2502.00023 Focus to learn more arXiv-issued DOI via DataCite

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