We describe a computer system that provides a real-time musi(cid:173) cal accompaniment for a live soloist in a piece of non-improvised music for soloist and accompaniment. A Bayesian network is devel(cid:173) oped that represents the joint distribution on the times at which the solo and accompaniment notes are played, relating the two parts through a layer of hidden variables. The network is first con(cid:173) structed using the rhythmic information contained in the musical score. The network is then trained to capture the musical interpre(cid:173) tations of the soloist and accompanist in an off-line rehearsal phase. During live accompaniment the learned distribution of the network is combined with a real-time analysis of the soloist's acoustic sig(cid:173) nal, performed with a hidden Markov model, to generate a musi(cid:173) cally principled accompaniment that respects all available sources of knowledge.

We describe a computer system that provides a real-time musical accompanimentfor a live soloist in a piece of non-improvised music for soloist and accompaniment. A Bayesian network is developed thatrepresents the joint distribution on the times at which the solo and accompaniment notes are played, relating the two parts through a layer of hidden variables. The network is first constructed usingthe rhythmic information contained in the musical score. The network is then trained to capture the musical interpretations ofthe soloist and accompanist in an off-line rehearsal phase. During live accompaniment the learned distribution of the network is combined with a real-time analysis of the soloist's acoustic signal, performedwith a hidden Markov model, to generate a musically principledaccompaniment that respects all available sources of knowledge. A live demonstration will be provided.