Expressive Performance Rendering: Introducing Performance Context

We present a performance rendering system that uses a probabilistic network to model dependencies between score and performance. The score context of a note is used to predict the corresponding performance characteristics. Two extensions to the system are presented, which aim at incorporating the current performance context into the prediction, which should result in more stable and consistent predictions. In particular we generalise the Viterbi-algorithm, which works on discrete-state Hidden Markov Models, to continuous distributions and use it to calculate the overall most probable sequence of performance predictions. The algorithms are evaluated and compared on two very large data-sets of human piano performances: 13 complete Mozart Sonatas and the complete works for solo piano by Chopin.