Artificially intelligent accompaniment using Hidden Markov Models to model musical structure

Background in Music Performance and Accompaniment. Musical accompanists may not always be available during practice, or the available accompanist may not have the technical ability necessary. As a solution to this problem, many musicians practise with pre-recorded accompaniment. Such an accompaniment is fixed and does not interact with the musician’s playing: the musician must adapt their performance to match the recording. It is more natural for the musician if the accompaniment adapts to fit the performer. To synchronise accompaniment with soloist, an accompanist should be able to follow the musician through the score as they play. Complications arise if the performer deviates from the score: either intentionally, by adding their own musical interpretation, or accidentally, by making performance errors. The accompanist should be able to adjust to such behaviour.