Multi-Phase Learning for Jazz Improvisation and Interaction

Abstract

This article presents a model for computational learning composed of two phases that enable a machine to interactively improvise jazz with a human. To explore and demonstrate this model, a working system has been built, called CHIME for Computer Human Interacting Musical Entity. In phase 1, a recurrent neural network is used to train the machine to reproduce 3 jazz melodies. Using this knowledge, CHIME can interactively play music with a human in real time by trading fours in jazz improvisation. The machine is further trained in phase 2 with a real-valued reinforcement learning algorithm. Included in the paper are details of the mechanisms for learning and interaction and the results. The paper presentation includes real-time demonstrations of CHIME.