Signal classification using wavelets and neural networks
Christopher M. Johnson, Edward W. Page, Gene A. Tagliarini · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
The ability of wavelet decomposition to reduce signals to a relatively small number of components can be exploited in pattern recognition applications. Several recent studies have shown that wavelet decomposition extracts salient signal features which can lead to improved pattern classification by a neural network. The performance of the neural network classifier is heavily dependent upon the ability of wavelet processing to yield discriminatory features. This paper considers the combination of wavelet and neural processing for classifying 1- dimensional signals embedded in noise. Noisy signals were decomposed using the Haar wavelet basis and feedforward neural networks were trained on wavelet series coefficients at various scales. The experiment was repeated using the 4-coefficient Daubechies wavelet basis. The classification accuracy for both wavelet bases is compared over multiple scales, several signal-to-noise ratios, and varying numbers of training epochs.