Classification using Hermite Basis Functions

A method of signal classification using Hermite polynomials for signal preprocessing is presented. Low frequency acoustic signals are preprocessed using a Hermite orthogonal basis inner product approach. The Hermite preprocessed signals result in feature vectors that are used as input to a parallel bank of radial basis function neural networks (RBFNN) for classification. The spread and threshold values for each of the RBFNN are then optimized. Robustness of this classification method is tested by introducing unknown events outside the training set and counting errors. The Hermite preprocessing method is shown to have superior performance compared to a standard cepstral preprocessing method.