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Publication

Connectionist Models for Musical Control of Nonlinear Dynamical Systems

D. Wessel, “Connectionist Models for Musical Control of Nonlinear Dynamical Systems”, Journal of Acoustical Society of America, vol. 92, 1992.

Musical instruments, whether acoustic or electronic, are often nonlinear. So too are many compositional algorithms used to generate sequences of notes. Effective artistic use of such instruments and compositional procedures requires controllers that transform the musician’s intention into the parameters that operate on the nonlinear system. Although a number of thorny theoretical issues remain unresolved, neural networks show considerable promise for the identification and control of nonlinear dynamical systems.

Publication

Real-Time Additive Synthesis Controlled by a Mixture of Neural-Networks and Direct Manipulation of Physical and Perceptual Attributes

A. Freed, Goldstein, M., Goodwin, M., Lee, M. A., McMillen, K., Rodet, X., Wessel, D., and Wright, M., “Real-Time Additive Synthesis Controlled by a Mixture of Neural-Networks and Direct Manipulation of Physical and Perceptual Attributes”, in International Computer Music Conference, Aarhus, Denmark, 1994, pp. 362-363.

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