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.

Inversions of statistical parameters of an acoustic signal in range-dependent environments with applications in ocean acoustic tomography.

The paper presents an application of a method for the characterization of underwater acoustic signals based on the statistics of the wavelet transform sub-band coefficients in range-dependent environments. As it was illustrated in previous work, this statistical characterization scheme is a very efficient tool for obtaining observables to be exploited in problems of ocean acoustic tomography and geoacoustic inversion, when range-independent environments are considered. Now the scheme is applied in range-dependent environments for the estimation of range-dependent features in shallow water. A simple denoising strategy, also presented in the paper, is shown to enhance the quality of the inversion results as it helps to keep the signal characterization to the energy a significant part of it. The results presented for typical test cases are encouraging and indicative of the potential of the method for the treatment of inverse problems in acoustical oceanography.

A statistical geoacoustic inversion scheme based on a modified radial basis functions neural network.

This paper addresses the task of recovering the geoacoustic parameters of a shallow-water environment using measurements of the acoustic field due to a known source and a neural network based inversion process. First, a novel efficient "observable" of the acoustic signal is proposed, which represents the signal in accordance with the recoverable parameters. Motivated by recent studies in non-Gaussian statistical theory, the observable is defined as a set of estimated model parameters of the alpha-stable distributions, which fit the marginal statistics of the wavelet subband coefficients, obtained after the transformation of the original signal via a one-dimensional wavelet decomposition. Following the modeling process to extract the observables as features, a radial basis functions neural network is employed to approximate the vector function that takes as input the observables and gives as output the corresponding set of environmental parameters. The performance of the proposed approach in recovering the sound speed and density in the substrate of a typical shallow-water environment is evaluated using a database of synthetic acoustic signals, generated by means of a normal-mode acoustic propagation algorithm.