The Experts below are selected from a list of 2466 Experts worldwide ranked by ideXlab platform
Angelique Dremeau - One of the best experts on this subject based on the ideXlab platform.
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dispersive grid free orthogonal matching pursuit for modal estimation in Ocean Acoustics
International Conference on Acoustics Speech and Signal Processing, 2020Co-Authors: Thomas Pavietsalomon, Julien Bonnel, Clement Dorffer, Barbara Nicolas, Thierry Chonavel, Angelique DremeauAbstract:Considering low-frequency acoustic sources, shallow-water environments act as modal dispersive waveguides. In this context, the signal can be described as a sum of a few modal components, each of them propagating with its own wavenumber. When dealing with broadband sources, wavenumber-frequency (f-k) diagrams constitute popular representations naturally enabling modal separation. Based on a Fourier transform, they require however a large number of sensors to resolve wavenumbers with a high-resolution. This limitation can be overcame by adding some physical priors to the processing method. In the continuation of previous works, we propose here a new grid-free algorithm allowing a super-resolution of the (f-k) diagram by benifiting from the sparse nature of the wavenumber spectrum and embedding the broadband behavior of the wavenumbers within the algorithm. The method is validated on simulated data.
Thomas Pavietsalomon - One of the best experts on this subject based on the ideXlab platform.
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dispersive grid free orthogonal matching pursuit for modal estimation in Ocean Acoustics
International Conference on Acoustics Speech and Signal Processing, 2020Co-Authors: Thomas Pavietsalomon, Julien Bonnel, Clement Dorffer, Barbara Nicolas, Thierry Chonavel, Angelique DremeauAbstract:Considering low-frequency acoustic sources, shallow-water environments act as modal dispersive waveguides. In this context, the signal can be described as a sum of a few modal components, each of them propagating with its own wavenumber. When dealing with broadband sources, wavenumber-frequency (f-k) diagrams constitute popular representations naturally enabling modal separation. Based on a Fourier transform, they require however a large number of sensors to resolve wavenumbers with a high-resolution. This limitation can be overcame by adding some physical priors to the processing method. In the continuation of previous works, we propose here a new grid-free algorithm allowing a super-resolution of the (f-k) diagram by benifiting from the sparse nature of the wavenumber spectrum and embedding the broadband behavior of the wavenumbers within the algorithm. The method is validated on simulated data.
Peter Gerstoft - One of the best experts on this subject based on the ideXlab platform.
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machine learning in Acoustics theory and applications
arXiv: Signal Processing, 2019Co-Authors: Michael J Bianco, Peter Gerstoft, James Traer, Emma Ozanich, Marie A Roch, Sharon Gannot, Charlesalban DeledalleAbstract:Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to Ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of Acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional Acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in Acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four Acoustics research areas: source localization in speech processing, source localization in Ocean Acoustics, bioAcoustics, and environmental sounds in everyday scenes.
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source localization in an Ocean waveguide using supervised machine learning
Journal of the Acoustical Society of America, 2017Co-Authors: Haiqiang Niu, Emma Reeves, Peter GerstoftAbstract:Source localization in Ocean Acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.
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source localization in an Ocean waveguide using supervised machine learning
arXiv: Atmospheric and Oceanic Physics, 2017Co-Authors: Haiqiang Niu, Emma Reeves, Peter GerstoftAbstract:Source localization in Ocean Acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix (SCM) and used as the input. Three machine learning methods (feed-forward neural networks (FNN), support vector machines (SVM) and random forests (RF)) are investigated in this paper, with focus on the FNN. The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization..
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An Overview of Sequential Bayesian Filtering in Ocean Acoustics
IEEE Journal of Oceanic Engineering, 2011Co-Authors: Caglar Yardim, Zoi-heleni Michalopoulou, Peter GerstoftAbstract:Sequential filtering provides a suitable framework for estimating and updating the unknown parameters of a system as data become available. The foundations of sequential Bayesian filtering with emphasis on practical issues are first reviewed covering both Kalman and particle filter approaches. Filtering is demonstrated to be a powerful estimation tool, employing prediction from previous estimates and updates stemming from physical and statistical models that relate acoustic measurements to the unknown parameters. Ocean acoustic applications are then reviewed focusing on source tracking, estimation of environmental parameters evolving in time or space, and frequency tracking. Spatial arrival time tracking is illustrated with 2006 Shallow Water Experiment data.
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Ocean acoustic inversion with estimation of a posteriori probability distributions
The Journal of the Acoustical Society of America, 1998Co-Authors: Peter Gerstoft, Christoph F. MecklenbraukerAbstract:Inversion methods are applied in Ocean Acoustics to infer parameters which characterize the environment. The objective of this paper is to provide such estimates, and means of evaluating the inherent uncertainty of the parameter estimates. In a Bayesian approach, the result of inversion is the a posteriori probability density for the estimated parameters, from which all information such as mean, higher moments, and marginal distributions can be extracted. These are multidimensional integrals of the a posteriori probability density, which are complicated to evaluate for many parameters. Various sampling options are examined and it is suggested that “importance sampling” based on a directed Monte Carlo method, such as genetic algorithms, is the preferred method. The formulation of likelihood functions and maximum-likelihood objective functions for multifrequency data on a vertical array is discussed. A priori information about the parameters may be used in the formulation. Shallow-water acoustic data obtain...
Dimitri Komatitsch - One of the best experts on this subject based on the ideXlab platform.
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broadband transmission losses and time dispersion maps from time domain numerical simulations in Ocean Acoustics
Journal of the Acoustical Society of America, 2018Co-Authors: Alexis Bottero, Paul Cristini, Dimitri Komatitsch, Quentin BrissaudAbstract:In this letter, a procedure for the calculation of transmission loss maps from numerical simulations in the time domain is presented. It can be generalized to arbitrary time sequences and to elastic media and provides an insight into how energy spreads into a complex configuration. In addition, time dispersion maps can be generated. These maps provide additional information on how energy is distributed over time. Transmission loss and time dispersion maps are generated at a negligible additional computational cost. To illustrate the type of transmission loss maps that can be produced by the time-domain method, the problem of the classical two-dimensional upslope wedge with a fluid bottom is addressed. The results obtained are compared to those obtained previously based on a parabolic equation. Then, for the same configuration, maps for an elastic bottom and maps for non-monochromatic signals are computed.
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an axisymmetric time domain spectral element method for full wave simulations application to Ocean Acoustics
Journal of the Acoustical Society of America, 2016Co-Authors: Alexis Bottero, Paul Cristini, Dimitri Komatitsch, Mark AschAbstract:The numerical simulation of acoustic waves in complex three-dimensional (3D) media is a key topic in many branches of science, from exploration geophysics to non-destructive testing and medical imaging. With the drastic increase in computing capabilities this field has dramatically grown in the last 20 years. However many 3D computations, especially at high frequency and/or long range, are still far beyond current reach and force researchers to resort to approximations, for example, by working in two dimensions (plane strain) or by using a paraxial approximation. This article presents and validates a numerical technique based on an axisymmetric formulation of a spectral finite-element method in the time domain for heterogeneous fluid-solid media. Taking advantage of axisymmetry enables the study of relevant 3D configurations at a very moderate computational cost. The axisymmetric spectral-element formulation is first introduced, and validation tests are then performed. A typical application of interest in Ocean Acoustics showing upslope propagation above a dipping viscoelastic Ocean bottom is then presented. The method correctly models backscattered waves and explains the transmission losses discrepancies pointed out in F. B. Jensen, P. L. Nielsen, M. Zampolli, M. D. Collins, and W. L. Siegmann, Proceedings of the 8th International Conference on Theoretical and Computational Acoustics (ICTCA) (2007). Finally, a realistic application to a double seamount problem is considered.
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An axisymmetric time-domain spectral-element method for full-wave simulations: Application to Ocean Acoustics
The Journal of the Acoustical Society of America, 2016Co-Authors: Alexis Bottero, Paul Cristini, Dimitri Komatitsch, Mark AschAbstract:The numerical simulation of acoustic waves in complex 3D media is a key topic in many branches of science, from exploration geophysics to non-destructive testing and medical imaging. With the drastic increase in computing capabilities this field has dramatically grown in the last twenty years. However many 3D computations, especially at high frequency and/or long range, are still far beyond current reach and force researchers to resort to approximations, for example by working in 2D (plane strain) or by using a paraxial approximation. This article presents and validates a numerical technique based on an axisymmetric formulation of a spectral finite-element method in the time domain for heterogeneous fluid-solid media. Taking advantage of axisymmetry enables the study of relevant 3D configurations at a very moderate computational cost. The axisymmetric spectral-element formulation is first introduced, and validation tests are then performed. A typical application of interest in Ocean Acoustics showing upslope propagation above a dipping viscoelastic Ocean bottom is then presented. The method correctly models backscattered waves and explains the transmission losses discrepancies pointed out in Jensen et al. (2007). Finally, a realistic application to a double seamount problem is considered.
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some illustrative examples of the use of a spectral element method in Ocean Acoustics
Journal of the Acoustical Society of America, 2012Co-Authors: Paul Cristini, Dimitri KomatitschAbstract:Some numerical results in the time domain obtained with the spectral-element method are presented in order to illustrate the high potential of this technique for modeling the propagation of acoustic waves in the Ocean in complex configurations. A validation for a simple configuration with a known solution is shown, followed by some simulations of the propagation of acoustic waves over different types of Ocean bottoms (fluid, elastic, and porous) to emphasize the wide variety of media that can be considered within the framework of this method. Finally, a movie illustrating upslope propagation over a viscoelastic wedge is presented and discussed.
Julien Bonnel - One of the best experts on this subject based on the ideXlab platform.
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dispersive grid free orthogonal matching pursuit for modal estimation in Ocean Acoustics
International Conference on Acoustics Speech and Signal Processing, 2020Co-Authors: Thomas Pavietsalomon, Julien Bonnel, Clement Dorffer, Barbara Nicolas, Thierry Chonavel, Angelique DremeauAbstract:Considering low-frequency acoustic sources, shallow-water environments act as modal dispersive waveguides. In this context, the signal can be described as a sum of a few modal components, each of them propagating with its own wavenumber. When dealing with broadband sources, wavenumber-frequency (f-k) diagrams constitute popular representations naturally enabling modal separation. Based on a Fourier transform, they require however a large number of sensors to resolve wavenumbers with a high-resolution. This limitation can be overcame by adding some physical priors to the processing method. In the continuation of previous works, we propose here a new grid-free algorithm allowing a super-resolution of the (f-k) diagram by benifiting from the sparse nature of the wavenumber spectrum and embedding the broadband behavior of the wavenumbers within the algorithm. The method is validated on simulated data.