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Identifier 000423989
Title Acoustic signal characterization using hidden markov models with applications in acoustical oceanography
Alternative Title Στατιστικός χαρακτηρισμός ακουστικών σημάτων με χρήση κρυπτο-μαρκοβιανών μοντέλων και εφαρμογές στην ακουστική οκεανογραφία
Author Σμαραγδάκης, Κωνσταντίνος
Thesis advisor Ταρουδάκης, Μιχαήλ
Reviewer Μακράκης, Γεώργιος
Τσόγκα, Χρυσούλα
Dosso, Stan
Χαρμανδάρης, ευάγγελος
Σκαρσούλης, Εμμανουήλ
Τζαγκαράκης, Γεώργιος
Abstract The scope of this PhD thesis is to develop a new probabilistic characterization scheme for acoustic signals recorded in the marine environment, with applications in acoustical oceanog- raphy. We will refer to the proposed scheme as Probabilistic Signal Characterization Scheme (PSCS). The schemes aims at the definition of a set of observables (signal features) that could characterize a signal to a unique way. To this end, a signal is decomposed into several levels using the stationary wavelet packet transform. This decomposition provides a time-frequency analysis of the characteristics of the signal. The stationary wavelet packet coefficients of the various levels are then modeled by a single left-to-right Hidden Markov Model (HMM) with Gaussian emission distributions. The concept behind the decision of using a sequential mod- eling of the signal’s extracted coefficients, was the fact that a signal after propagation through a dispersive medium such as water column in the marine environment, exhibits evolving time-frequency characteristics. The association of a signal with a representative HMM is per- formed by means of the Expectation-Maximization (EM) algorithm. Eventually the signal is characterized by a set of parameters which describe the HMM. The proposed signal characterization methods has been applied in inverse problems of acous- tical oceanography. In particular, problems associated with the retrieval of the marine environ- mental parameters using measured features of the acoustic field due to a sound source have been considered. These problems being in nature non-linear are solved with optimization pro- cedures requiring comparison of the characteristic of the measured acoustic signal with same of replica signals. In this work 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, the thesis presents few characteristic test cases in which simulated and real data have been considered. The measured signals are characterized by means of the proposed PSCS method and the model parameters of the marine environment have been estimated by employing a Genetic Algorithm (GA) over three sets of population of candidate model parameters. The GA leads to distributions of the model parameters of the vii final population using Gaussian Mixture Model (GMM). This representation provides the so- lutions of the inverse problems in the form of the maximum of the marginal densities and a qualitative indication of the confidence intervals of the recoverable parameters. The results are compared with those obtained using the Statistical Signal Characterization Scheme (SSCS) proposed by Taroudakis et al. In addition, the results corresponding to the experimental data are compared to various approaches from the literature. The applications presented here con- firmed the reliability and efficiency of the method when applied with typical signals used in acoustical oceanography.
Language English
Subject Machine learning
Probabilistic models
Ακουστική διάδοση
Μηχανική μάθηση
Πιθανοθεωρητικά μοντέλα
Issue date 2019-07-26
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Applied Mathematics--Doctoral theses
  Type of Work--Doctoral theses
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