The article presents an application of instance-based learning to the problem of expressive music performance. A system is described that tries to learn to shape tempo and dynamics of a musical performance by analogy to timing and dynamics patterns found in performances by a concert pianist. The learning algorithm itself is a straightforward k-nearest-neighbour algorithm. The interesting aspects of this work are application-specific: we show how a complex, multi-level artifact like the tempo/dynamics variations applied by a musician can be decomposed into well-defined training examples for a learner, and that case-based learning is indeed a sensible strategy in an artistic domain like music performance. While the results of a first quantitative experiment turn out to be rather disappointing, we will show various ways in which the results can be improved, finally resulting in a system that won a prize in a recent 'computer music performance' contest.
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