Proceedings of the 26th annual conference on Computer graphics and interactive techniques
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
An entropic estimator for structure discovery
Proceedings of the 1998 conference on Advances in neural information processing systems II
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
An Interactive Case-Based Reasoning Approach for Generating Expressive Music
Applied Intelligence
Learning kernel-based HMMs for dynamic sequence synthesis
Graphical Models - Special issue on Pacific graphics 2002
Automatic identification of music performers with learning ensembles
Artificial Intelligence
Monte Carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research
Learning to play like the great pianists
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Trained musicians intuitively produce expressive variations that add to their audience's enjoyment. However, there is little quantitative information about the kinds of strategies used in different musical contexts. Since the literal synthesis of notes from a score is bland and unappealing, there is an opportunity for learning systems that can automatically produce compelling expressive variations. The ESP (Expressive Synthetic Performance) system generates expressive renditions using hierarchical hidden Markov models trained on the stylistic variations employed by human performers. Furthermore, the generative models learned by the ESP system provide insight into a number of musicological issues related to expressive performance.