Modeling, analyzing, and synthesizing expressive piano performance with graphical models
Identifieur interne : 000E57 ( Istex/Corpus ); précédent : 000E56; suivant : 000E58Modeling, analyzing, and synthesizing expressive piano performance with graphical models
Auteurs : Graham Grindlay ; David HelmboldSource :
- Machine Learning [ 0885-6125 ] ; 2006-12-01.
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Abstract
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.
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DOI: 10.1007/s10994-006-8751-3
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ISTEX:336D03B5CF5AAA162803103B22CF8E6477699D51Le document en format XML
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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. 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