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Abstract:

A number of conducting gesture analysis and performance systems have been developed over the years. However, most of the previous projects either primarily concentrated on tracking tempo and amplitude indicating gestures, or implemented individual mapping techniques for expressive gestures that varied from research to research. There is a clear need for a uniform pro- cess that could be applied toward analysis of both indicative and expressive gestures. The proposed system provides a set of tools that contain extensive functionality for identification, classification and performance with con- ducting gestures. Gesture recognition procedure is designed on the basis of Hidden Markov Model (HMM) process. A set of HMM tools are developed for Max/MSP software. Training and recognition procedures are applied to- ward both right hand beat- and amplitude- indicative gestures, and left hand expressive gestures. Continuous recognition of right-hand gestures is incorporated into a real-time gesture analysis and performance system in Eyesweb and Max/MSP/ Jitter environments.

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IDMIL Participants:

• Paul Kolesnik

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PaulKolesnik_MAthesis