@conference {181,
	title = {Neural networks for simultaneous classification and parameter estimation in musical instrument control},
	booktitle = {Adaptive and Learning Systems},
	series = {Proceedings of the SPIE - The International Society for Optical Engineering},
	volume = {1706},
	year = {1992},
	month = {1992/04/16},
	pages = {244-55},
	address = {Orlando, FL, USA},
	abstract = {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.},
	keywords = {machine learning, neural networks},
	doi = {10.1117/12.139949},
	url = {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},
	author = {Lee, Michael and Freed, Adrian and Wessel, David}
}
