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Abstract:
We describe a computer system that provides a real-time
musical accompaniment for a live soloist in a piece of
non-improvised music for soloist and accompaniment. A Bayesian
network is developed 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 constructed using the rhythmic information
contained in the musical score. The network is then trained to
capture the musical interpretations 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 signal,
performed with a hidden Markov model, to generate a musically
principled accompaniment that respects all available sources of
knowledge. A live demonstration will be provided.
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