Musical agents are AI software making music. Musical Agent based on Self-Organizing Maps (MASOM) is a musical software agent for live performance. MASOM plays experimental music and free improvisation. It learns by listening to audio files such as recordings of performances or compositions. We can train a MASOM agent on a set of music that is so big that it would take more than one human life to listen. Similarly, we can train MASOM agents on dead composers and convert their fixed media piece to interactive musical agents.
MASOM also extracts higher level features such as eventfulness, pleasantness, and timbre to understand the musical form of what it hears. MASOM is limited to the style of what is has listened to and reacts in real-time to what he is hearing. The agent can listen to itself and other performers to decide what to play next. MASOM is designed by Kıvanç Tatar and Philippe Pasquier. Publications:
->Tatar K., Pasquier P., & Siu R. (2018). REVIVE: An audio-visual performance with musical and visual Artificial Intelligence Agents. CHI’18, April 21–26, 2018, Montreal, QC, Canada ACM 978-1-4503-5621-3/18/04.
->Tatar, K. & Pasquier, P. (2017). MASOM: A Musical Agent Architecture based on Self-Organizing Maps, Affective Computing, and Variable Markov Models. In Proceedings of the 5th International Workshop on Musical Metacreation (MuMe 2017). Paper
MASOM version 0.06
Patar @Barely Constrained by CoCreaTive
Comparing MASOM's output to random segment playback:
In this video, we explore the memory of a MASOM trained on Bernhard Parmegiani's De Natura Sonorum.