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.

Four classes (NE= non-expressive, EO= expressive-onset, EV= expressive-velocity, and EP= expressively-performed) using heuristics for the expressive performance detection of MIDI tracks in GigaMIDI.

Automatic music video generation

MMM-synthrider is a musical entry to the ai song contest 2022 using calliope to interact with the mmm model