Neural network learning paradigms involving nonlinear spectral processing
Okan K. Ersoy, D. Hong · International Conference on Acoustics, Speech, and Signal Processing · 2003
Two neural network architectures involving nonlinear spectral transformations are described. The first architecture involves generalization of nonlinear matched-filtering techniques, yielding a network that is very fast in learning and recall as well as highly accurate in classification. The second architecture is hierarchical with a number of stages; after each stage, error detection is carried out, followed by nonlinear spectral transformations when the error measure is above threshold.>