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Cluster-Weighted Modeling: Probabilistic Time Series Prediction, Characterization, and Synthesis

  • Bernd Schoner
  • Neil Gershenfeld
Chapter

Abstract

Cluster-weighted modeling, a mixture density estimator around local models, is presented as a framework for the analysis, prediction and characterization of non-linear time series. First architecture, model estimation and characterization formalisms are introduced. The characterization tools include estimator uncertainty, predictor uncertainty, and the correlation dimension of the data set. in the second part of this chapter the framework is extended to synthesize audio signals and is applied to model a violin in a data-driven input-output approach.

Keywords

Local Model Audio Signal Audio Data Cluster Parameter Finger Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Bernd Schoner
  • Neil Gershenfeld

There are no affiliations available

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