Paul Kolesnik and Marcelo M. Wanderley, McGill University
A set of discrete Hidden Markov Model (HMM) objects have been developed for Max/MSP software as part of the project that deals with the analysis of expressive movements of a conductor. The objects were tested with recognition of English alphabet symbols, and were applied toward analysis of isolated conducting gestures. Training and recognition procedures were applied toward both right hand beat- and amplitude- indicative gestures (beat and tempo indications), and left hand expressive gestures (articulation indications). Recognition of right-hand gestures was incorporated into a real-time gesture analysis and performance system that was implemented in Eyesweb and Max/MSP/Jitter environments.