Acoustic signal characterization based on hidden Markov models with applications to geoacoustic inversions

A probabilistic characterization scheme for acoustic signals with applications in acoustical oceanography is presented. This scheme aims at the definition of a set of stochastic observables that could characterize the signal. To this end, the signal is decomposed into several levels using the stationary wavelet packet transform. The extracted wavelet coefficients are then modeled by a hidden Markov model (HMM) with Gaussian emission distributions. The association of a signal with a representative HMM is performed utilizing the expectation-maximization algorithm. Eventually, the signal is characterized by the set of parameters that describe the HMM. The Kullback-Leibler divergence is employed as the similarity measure of two signals, comparing their corresponding HMMs. To validate the performance of the proposed characterization scheme, which is denoted as the probabilistic signal characterization scheme (PSCS), a simulated and a real experiment have been considered. The measured signal is characterized by the proposed PSCS method, and the model parameters of the seabed are estimated by means of an inversion procedure employing a genetic algorithm. The inversion results confirmed the reliability and efficiency of the proposed method when applied with typical signals used in applications of acoustical oceanography.