Expressive Performance Rendering with Probabilistic Models

  • Sebastian Flossmann
  • Maarten Grachten
  • Gerhard Widmer
Chapter

Abstract

We present YQX, a probabilistic performance rendering system based on Bayesian network theory. It models dependencies between score and performance and predicts performance characteristics using information extracted from the score. We discuss the basic system that won the Rendering Contest RENCON 2008 and then present several extensions, two of which aim to incorporate the current performance context into the prediction, resulting in more stable and consistent predictions. Furthermore, we describe the first steps towards a multilevel prediction model: Segmentation of the work, decomposition of tempo trajectories, and combination of different prediction models form the basis for a hierarchical prediction system. The algorithms are evaluated and compared using two very large data sets of human piano performances: 13 complete Mozart sonatas and the complete works for solo piano by Chopin.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Sebastian Flossmann
    • 1
  • Maarten Grachten
    • 2
  • Gerhard Widmer
    • 3
    • 4
  1. 1.Department of Computational PerceptionJohannes Kepler UniversitatLinzAustria
  2. 2.Post Doctoral Researcher at the Department of Computational PerceptionJohannes Kepler UniversityLinzAustria
  3. 3.Department of Computational PerceptionJohannes Kepler University (JKU)LinzAustria
  4. 4.The Austrian Research Institute for Artificial Intelligence (OFAI)ViennaAustria

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