MACAT and MACataRT: New AI Systems for Co-Creative Music Performance
Two innovative musical agent systems, MACAT and MACataRT, have been unveiled by researchers to enhance collaborative music performance and improvisation between humans and AI. MACAT focuses on agent-led performances, utilizing real-time synthesis and self-listening to independently refine its output. In contrast, MACataRT provides a versatile setting for joint improvisation through audio mosaicing and sequence-based learning. Both systems are trained on tailored, small datasets, promoting ethical AI engagement while maintaining artistic integrity. This research, detailed in a paper on arXiv (arXiv:2502.00023v2), emphasizes the role of interactive, artist-centered generative AI in broadening creative avenues for musicians in live performance. It advocates for a transition toward AI tools that prioritize user control and ethical practices in the arts.
Key facts
- Two musical agent systems, MACAT and MACataRT, have been developed for co-creative music performance.
- MACAT is optimized for agent-led performance, using real-time synthesis and self-listening.
- MACataRT provides a flexible environment for collaborative improvisation through audio mosaicing and sequence-based learning.
- Both systems train on personalized, small datasets to ensure ethical and transparent AI engagement.
- The research emphasizes artist-centred generative AI to expand creative possibilities in real-time performance.
- The paper is available on arXiv with identifier 2502.00023v2.
- The announcement type is replace-cross, indicating a revised version.
- The systems are designed as human-in-the-loop generative AI for music.
Entities
Institutions
- arXiv