A DEMONSTRATION OF BOW ARTICULATION RECOGNITION WITH WEKINATOR AND K-BOW

Using the Wekinator software tool for real-time, interactive machine learning [3] and the K-Bow commercial sensor bow [5], we have constructed a real-time cello bow articulation classification system. This system is capable of outputting articulation labels (e.g., “legato,” “marcato,” “spiccato”) in real-time as a cellist performs. These labels, which are output via Open Sound Control [9], may be used in conjunction with visualization or music tools in composition and live performance. Our work is distinguished from prior work in bow gesture recognition in that the Wekinator allows a musician user to rapidly build customized bow gesture models from scratch by demonstrating bowing gestures to form a training set; the user can also interactively refine these models through iterative changes to both the learning algorithms and dataset. In this paper, we briefly describe our work creating articulation models for our own use. In particular, we show that the Wekinator and K-Bow together allowed for the fast creation of accurate models. We then propose a hands-on demonstration of this work in which ICMC attendees can use the K-Bow to interactively build their own gesture classifiers.