About

I am a PhD student at the Mixed Reality Lab at the University of Nottingham in the UK Turing AI fellowship "Somabotics: Creatively Embodying AI".

My research focuses on live music performance systems and their evaluation (see LOERIC), practice-led research methodologies, ethical issues and frictions between traditional communities and AI, and artificial entertainment (see KSPR).

I was previously based in Stockholm at KTH Royal Institute of Technology within the ERC project MUSAiC: Music at the Frontiers of Artificial Creativity and Criticism.

Peer-reviewed publications

  • 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”. AAAI 2026. Lecture Notes in Computer Science (forthcoming).
  • Amerotti, M., Benford, S., Sturm, B., Vear, C. “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.
  • Amerotti, M., Benford, S., Sturm, B., Avila, J. M. “The Virtual Session: Synchronizing Multiple Virtual Musicians Simulating an Irish Traditional Music Session”. Proceedings from ICMC'25, the International Computer Music Conference, 2025.
  • Benford, S., Amerotti, M., Sturm, B., Avila, J. M. “Negotiating Autonomy and Trust when Performing with an AI Musician”. Proceedings from TAS’24, the International Symposium on Trustworthy Autonomous Systems, 2024.
  • Sturm, B., Amerotti, M., Dalmazzo, D., Cros Vila, L., Casini, L., Kanhov, E. “Stochastic Pirate Radio (KSPR): Generative AI applied to simulate community radio”. Proceedings from AIMC’24, the International Conference on AI and Musical Creativity, 2024.
  • Amerotti, M., Sturm, B., Benford, S., Maruri-Aguilar H., Vear, C. “Evaluation of an Interactive Music Performance System in the Context of Irish Traditional Dance Music”. Proceedings from NIME’24, the International Conference on New Interfaces for Musical Expression, 2024.
  • Amerotti, M., Benford, S., Sturm, B., Vear, C. “A Live Performance Rule System arising from Irish Traditional Dance Music”. Proceedings from CMMR’23, the 16th International Symposium on Computer Music Multidisciplinary Research, 2023.

Workshops & Presentations

  • Benford, S., Amerotti, M., Sturm, B., Vear, C. “Performing with AI as a Practice-led Methodology for AI Music”. Presentation and performance at AIMS’24, the International Conference on AI Music Studies, 2024.
  • Amerotti, M., Sturm, B. “Endless generative AI: a practical tutorial”. Presented at AIMC’24, the International Conference on AI and Musical Creativity, 2024.
  • Casini, L., Amerotti, M. “Modeling composition and performance of traditional music with AI”.Presented at ICCC’24, the 15th International Conference on Computational Creativity, 2024.

Theses

  • Amerotti, M. "On the intelligence and creativity of an artificial intelligence system for traditional music." Tesi di laurea in Filosofia, Alma Mater Studiorum, Università di Bologna (Bologna, Italy), 2025.
  • Amerotti, M. "Modeling interactive performance of traditional music. Challenges and perspectives." Master's Thesis in Computer Science, KTH Royal Institute of Technology (Stockholm, Sweden), 2024.
  • Amerotti, M. "Latent representations for traditional music analysis and generation." Tesi di laurea in Informatica, Alma Mater Studiorum, Università di Bologna (Bologna, Italy), 2022.