Data-driven Modeling and Synthesis of Acoustical Instruments

Abstract

We present a framework for the analysis and synthesis of acoustical instruments based on data driven probabilistic inference modeling Audio time series and boundary conditions of a played instrument are recorded and the non linear mapping from the control data into the audio space is inferred using the general inference framework of Cluster Weighted Modeling The resulting model is used for real time synthesis of audio sequences from new input data

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Cite this paper

@inproceedings{Schner1998DatadrivenMA, title={Data-driven Modeling and Synthesis of Acoustical Instruments}, author={Bernd Sch{\"{o}ner and Chuck Cooper and Chris Douglas and Neil Gershenfeld}, booktitle={ICMC}, year={1998} }