Informing bowing and violin learning using movement analysis and machine learning

Violin performance is characterized by an intimate connection between the player and her instrument that allows her a continuous control of sound through a sophisticated bowing technique. A great importance in violin pedagogy is, then, given to techniques of the right hand, responsible of most of the sound produced. This study analyses the bowing trajectory in three different classical violin exercises from audio and motion capture recordings to classify, using machine learning techniques, the different kinds of bowing techniques used. Our results show that a clustering algorithm is able to appropriately group together the different shapes produced by the bow trajectories.