Bowing modeling for violin students assistance

Abstract

Though musicians tend to agree on the importance of practicing expressivity in performance, not many tools and techniques are available for the task. A machine learning model is proposed for predicting bowing velocity during performances of violin pieces. Our aim is to provide feedback to violin students in a technology--enhanced learning setting. Predictions are generated for musical phrases in a score by matching them to melodically and rhythmically similar phrases in performances by experts and adapting the bow velocity curve measured in the experts' performance. Results show that mean error in velocity predictions and bowing direction classification accuracy outperform our baseline when reference phrases similar to the predicted ones are available.