Exitium
Dubstep
- 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.
- Benford, S., Amerotti, M., Sturm, B., Martinez Avila, J. “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.
- 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.
- Amerotti, M. "Modeling interactive performance of traditional music. Challenges and perspectives." Master's Thesis, KTH Royal Institute of Technology (Stockholm, Sweden), 2024.
- Amerotti, M. "Latent representations for traditional music analysis and generation." Tesi di laurea, Alma Mater Studiorum, Università di Bologna (Bologna, Italy), 2022.
