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We propose a statistical procedure to characterize and extract features froma waveform that can be applied as a pre-processing signal stage in a patternrecognition task using Artificial Neural Networks. Such a procedure is based onmeasuring a 30-parameters set of moments and cumulants from the waveform, itsderivative, and its integral. The technique is presented as an extension of theStatistical Signal Characterization method existing in the literature.  As a testing methodology, we used the procedure to distinguish a pulse-likesignal from different versions of itself with frequency spectrum alterations ordeformations. The recognition task was performed by single feed-forwardback-propagation networks trained for the case Sinc-, Gaussian-, andChirp-pulse waveform. Because of the success obtained in these examples, we canconclude that the proposed extended statistical signal characterization methodis an effective tool for pattern-recognition applications. In particular, wecan use it as a fast pre-processing stage in embedded systems with limitedmemory or computational capability.

Extended method for Statistical Signal Characterization using moments  and cumulants: Application to recognition of pattern alterations in  pulse-like waveforms employing Artificial Neural Networks