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.
Cite
@article{arxiv.2502.00023,
title = {Musical Agent Systems: MACAT and MACataRT},
author = {Keon Ju M. Lee and Philippe Pasquier},
journal= {arXiv preprint arXiv:2502.00023},
year = {2025}
}
Comments
In Proceedings of the Creativity and Generative AI NIPS (Neural Information Processing Systems) Workshop 2024