This is an ‘on-the-fly’ musical collaboration between human and machine. The musician's improvisation is encoded as statistical behaviours that the computer assimilates by training, in real-time. The system maps its judgements to a library of sonic materials and stochastic behaviours. (This is the only ‘composed’ element of the performance). Recurring aspects of the player’s performance can then be recognised and ‘appraised’ by the computer, which adapts to changes in the improvisation as it learns more about the player’s behaviour. So, the machine expresses its recognition and creative response to the player by developing, and modifying, its own musical output, just as another player might. Both ‘musicians’ adapt to each other as the performance develops. This version uses a more efficient, feature-space model for machine learning.

https://research.gold.ac.uk/id/eprint/7743