Spectral feature extraction using Poisson moments

We propose to use the Gamma filter 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-frequency 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 correspond 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 computer. 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