An approach to predicting bowing control parameter contours in violin performance

Abstract

We present a machine learning approach to modeling bowing control parameter contours in violin performance. Using accurate sensing techniques we obtain relevant timbre-related bowing control parameters such as bow transversal velocity, bow pressing force, and bow-bridge distance of each performed note. Each performed note is represented by a curve parameter vector and a number of note classes are defined. The principal components of the data represented by the set of curve parameter vectors are obtained for each class. Once curve parameter vectors are expressed in the new space defined by the principal components, we train a model based on inductive logic programming, able to predict curve parameter vectors used for rendering bowing controls. We evaluate the prediction results and show the potential of the model by predicting bowing control parameter contours from an annotated input score.