Data-driven modeling of acoustical instruments

We present a framework for the analysis and synthesis of acoustical instrum ents based on data-driven probabilistic inference modeling. Audio time seri es 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 resultin g model is used for real-time synthesis of audio sequences from new input d ata.