SPECTRAL FEATURE EXTRACTION USING POISSON MOMENTS Samel Çelebi, Jose C. Principe Computational Neuroengineering Lab. CSE447 University of Florida, Gainesville FL32611, USA E-Mail: celebi@synapse.ee.ufl.edu, principe@synapse.ee.ufl.edu Abstract. We propose to use the Gamma filter [1] as a feature extractor for the preprocessing of speech signals. Gamma filter which can be implemented as a cascade of identical first order lowpass filters generates at its taps the Poisson Moments of an input signal. These moments carry spectral information about the recent history of the input signal. They can be used to construct time-fre- quency representations as an alternative to the conventional methods of short term Fourier transform, cepstrum, etc. In this study it is shown that when the time scale of the Gamma filter is chosen properly, the Poisson moments corre- spond to the Taylor’s series expansion coefficients of the input signal spectra. The appeal of the proposed method comes from the fact that in the analog domain the moments are available as a continuous time electrical signal and can be physically measured, rather than computed off-line by a digital com- puter. With this convenience, the speed of the discrete time processor following the preprocessor is independent of the highest frequency of the input signal, but is constrained with the stationarity duration of the signal. INTRODUCTION Classification of temporal patterns is one of the areas where artificial neural net- works (ANNs) are frequently utilized. Speech recognition is a special case to that problem. In order to simplify the classification task undertaken by an ANN prepro- cessing of the temporal pattern is vital. The goal of the preprocessing should be to capture the features of the pattern and to express them in a low dimensional space. If this is achieved, then a big deal of computational and structural burden over the neural network can be removed. One method suggested for the preprocessing of speech signals is the Focused Gamma Network [2][3]. This is a generalized feedforward structure with adjustable feedback which is responsible for changing the time scale (or the memory depth) of the preprocessor. Adjusting the time scale allows one to focus the representation space on the signal of interest such that a low dimensional, but a faithful representa-