Cello bowing techniques are classified by applying supervised machine learning methods to sensor data from two inertial sensors called the Orient specks – one worn on the playing wrist and the other attached to the frog of the bow. Twelve different bowing techniques were considered, including variants on a single string and across multiple strings. Results are presented for the classification of these twelve techniques when played singly, and in combination during improvisational play. The results demonstrated that even when limited to two sensors, classification accuracy in excess of 95% was obtained for the individual bowing styles, with the added advantages of a minimalist approach.
|Title of host publication||BodyNets '15 Proceedings of the 10th EAI International Conference on Body Area Networks|
|Publisher||ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)|
|Number of pages||7|
|Publication status||Published - 2015|