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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.00023 |
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| _version_ | 1866909471773556736 |
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| author | Lee, Keon Ju M. Pasquier, Philippe |
| author_facet | Lee, Keon Ju M. Pasquier, Philippe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Musical Agent Systems: MACAT and MACataRT Lee, Keon Ju M. Pasquier, Philippe Multiagent Systems Artificial Intelligence Human-Computer Interaction Sound Audio and Speech Processing 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. |
| title | Musical Agent Systems: MACAT and MACataRT |
| topic | Multiagent Systems Artificial Intelligence Human-Computer Interaction Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2502.00023 |