Marco Amerotti

Contact

• work
• Marco.Amerotti@nottingham.ac.uk

Biography

Marco Amerotti is a PhD student at the Mixed Reality Lab at the University of Nottingham in the UK Turing AI fellowship "Somabotics: Creatively Embodying AI"; within the fellowship, he is responsible for the "Traditionable Machines" project.

He previously worked as a researcher at the Royal Institute of Technology in Stockholm within the ERC project "MUSAiC: Music at the Frontiers of Artificial Creativity and Criticism". He obtained an MSc in Computer Science from KTH Royal Institute of Technology in Stockholm, Sweden, and a BSc in Computer Science and a BSc in Philosophy from the University of Bologna, Italy.

Research Summary

His research focuses on musical AI, particularly on AI-generated, interactive co-performance of Irish traditional dance music. He developed the music performance system "LOERIC", which has been… read more

Recent Publications

• AMEROTTI, MARCO, 2026. TradJockey: Live Remixing a Performance System for Traditional Music In: Creative AI for Live Interactive Performances.
• AMEROTTI, MARCO, BENFORD, STEVE, STURM, BOB L. T. and VEAR, CRAIG, 2026. A Live Performance Rule System Informed by Irish Traditional Dance Music In: Music and Sound Generation in the AI Era. 127-139
• AMEROTTI, MARCO, BENFORD, STEVE, STURM, BOB L. T. and AVILA, JUAN MARTINEZ, 2025. The Virtual Session: Synchronizing Multiple Virtual Musicians Simulating an Irish Traditional Music Session In: Proceedings of the International Computer Music Conference.
• AMEROTTI, MARCO, STURM, BOB, BENFORD, STEVE, MARURI-AGUILAR, HUGO and VEAR, CRAIG, 2024. Evaluation of an Interactive Music Performance System in the Context of Irish Traditional Dance Music In: New Interfaces for Musical Expression (NIME), NIME’24, 4-6 September, Utrecht, The Netherlands.

• View all publications

• Audio examples accompanying Amerotti et al. "A Live Performance Rule System Informed by Irish Traditional Dance Music". In: Ystad, S., Kronland-Martinet, R., Kitahara, T., Hirata, K., Aramaki, M. (eds) "Music and Sound Generation in the AI Era". CMMR 2023. Lecture Notes in Computer Science, vol 15236, 2026.
• Audio examples accompanying Amerotti et al. "The Virtual Session: Synchronizing Multiple Virtual Musicians Simulating an Irish Traditional Music Session". Proceedings from ICMC'25, the International Computer Music Conference, 2025.
• Audio examples accompanying Amerotti, M. "TradJockey: Live Remixing a Performance System for Traditional Music". In: Woodward, K., Falcon-Caro, A., Ramchurn, R., Benford, S. (eds) "Creative AI for Live Interactive Performanes workshop 2026". CLIP 2026. Communications in Computer and Information Science, vol 2865, 2026.

Current Research

His research focuses on musical AI, particularly on AI-generated, interactive co-performance of Irish traditional dance music. He developed the music performance system "LOERIC", which has been featured in a variety of publications and concerts in Sweden and the UK.

• AMEROTTI, MARCO, 2026. TradJockey: Live Remixing a Performance System for Traditional Music In: Creative AI for Live Interactive Performances.
• AMEROTTI, MARCO, BENFORD, STEVE, STURM, BOB L. T. and VEAR, CRAIG, 2026. A Live Performance Rule System Informed by Irish Traditional Dance Music In: Music and Sound Generation in the AI Era. 127-139
• AMEROTTI, MARCO, BENFORD, STEVE, STURM, BOB L. T. and AVILA, JUAN MARTINEZ, 2025. The Virtual Session: Synchronizing Multiple Virtual Musicians Simulating an Irish Traditional Music Session In: Proceedings of the International Computer Music Conference.
• AMEROTTI, MARCO, STURM, BOB, BENFORD, STEVE, MARURI-AGUILAR, HUGO and VEAR, CRAIG, 2024. Evaluation of an Interactive Music Performance System in the Context of Irish Traditional Dance Music In: New Interfaces for Musical Expression (NIME), NIME’24, 4-6 September, Utrecht, The Netherlands.
• BENFORD, STEVE, AMEROTTI, MARCO, STURM, BOB L. T. and MARTINEZ AVILA, JUAN, 2024. Negotiating Autonomy and Trust when Performing with an AI Musician In: Proceedings of the Second International Symposium on Trustworthy Autonomous Systems. 1-10
• STURM, BOB, AMEROTTI, MARCO, DALMAZZO, DAVID, CROS VILA, LAURA, CASINI, LUCA and KANHOV, ELIN, 2024. Stochastic Pirate Radio (KSPR): Generative AI applied to simulate commercial radio In: AI Music Creativity, AIMC 2024, 9-11 September.