%0 Conference Paper
%B Adaptive and Learning Systems
%D 1992
%T Neural networks for simultaneous classification and parameter estimation in musical instrument control
%A Lee, Michael
%A Freed, Adrian
%A Wessel, David
%C Orlando, FL, USA
%K machine learning
%K neural networks
%P 244-55
%R 10.1117/12.139949
%S Proceedings of the SPIE - The International Society for Optical Engineering
%U https://www.spiedigitallibrary.org/conference-proceedings-of-spie/1706/1/Neural-networks-for-simultaneous-classification-and-parameter-estimation-in-musical/10.1117/12.139949.short?SSO=1
%V 1706
%X In this report we present our tools for prototyping adaptive user interfaces in the context of real-time musical instrument control. Characteristic of most human communication is the simultaneous use of classified events and estimated parameters. We have integrated a neural network object into the MAX language to explore adaptive user interfaces that considers these facets of human communication. By placing the neural processing in the context of a flexible real-time musical programming environment, we can rapidly prototype experiments on applications of adaptive interfaces and learning systems to musical problems. We have trained networks to recognize gestures from a Mathews radio baton, Nintendo Power GloveTM, and MIDI keyboard gestural input devices. In one experiment, a network successfully extracted classification and attribute data from gestural contours transduced by a continuous space controller, suggesting their application in the interpretation of conducting gestures and musical instrument control. We discuss network architectures, low-level features extracted for the networks to operate on, training methods, and musical applications of adaptive techniques.
%8 1992/04/16