Classification of chirps using Hidden Markov Models

This paper addresses the problem of classifying chirp signals in noise. Our basic approach combines a Short Time Fourier Transform (STFT) with a Hidden Markov Model (HMM) to track the frequency progression versus time. Next, the best-fit polynomial of the resulting discrete Viterbi path is computed or the central moments are estimated from the distribution of the path. Our experimental results show that separable clusters in the feature space are formed for broad classes of chirps. A Bayesian Classifier can then be applied effectively to classify the different families of chirps. Experiments have been carried out on both synthetically generated chirp signals and naturally occurring lightning discharges as recorded by the FORTE satellite.