The Speckled Cellist: Classification of Cello Bowing Styles using the Orient Specks

Debadri Mukherjee, D K Arvind

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationBodyNets '15 Proceedings of the 10th EAI International Conference on Body Area Networks
PublisherICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)
Pages25-31
Number of pages7
ISBN (Print)978-1-63190-084-6
DOIs
Publication statusPublished - 2015

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