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Machine Learning

, Volume 65, Issue 2–3, pp 361–387 | Cite as

Modeling, analyzing, and synthesizing expressive piano performance with graphical models

  • Graham GrindlayEmail author
  • David Helmbold
Article

Abstract

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.

Keywords

Graphical models Hierarchical hidden Markov models Music performance Musical information retrieval 

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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Media LaboratoryMassachusetts Institute of TechnologyCambridge
  2. 2.Computer Science DeptartmentUniversity of CaliforniaSanta Cruz

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