☆ 4.5 Article

Bayesian optimization with Gaussian process surrogate model for source localization

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Acoustics

Graph-guided Bayesian matrix completion for ocean sound speed field reconstruction

Siyuan Li et al.

Summary: Reconstructing ocean sound speed field is crucial for various ocean acoustic applications, but traditional methods have drawbacks in terms of noise sensitivity and computational cost. To overcome these limitations, this paper proposes a graph-guided Bayesian low-rank matrix completion method for accurate reconstruction of ocean sound speed field, which balances reconstruction accuracy and computational complexity by simultaneously utilizing global (low-rankness) and local (graph structure) information.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2023)

添加到收藏夹

Article Acoustics

Inversion in an uncertain ocean using Gaussian processes

Zoi-Heleni Michalopoulou et al.

Summary: This work utilizes Gaussian processes (GPs) to capture correlation of the acoustic field at different depths in the ocean for pre-processing acoustic data in underwater waveguide. The data are denoised and interpolated using GPs, generating densely populated acoustic fields at virtual arrays for source localization and environmental inversion. Field predictions are made by computing replicas at virtual receivers, and the correlations among field measurements are selected through kernel functions with estimated hyperparameters. The approach is found to be superior to conventional beamformer MFI and the Matern kernel is preferred over the Gaussian kernel.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2023)

添加到收藏夹

Article Acoustics

Joint trans-dimensional inversion for water-column sound speed and seabed geoacoustic models

Stan E. Dosso et al.

Summary: This letter discusses the joint estimation of the water-column sound-speed profile and seabed geoacoustic model through Bayesian inversion of ocean-acoustic data. Separate trans-dimensional models are used for the water column and seabed to parameterize each according to the data's information content. The method is then validated using modal-dispersion data from the New England Mud Patch, collected using hand-deployable systems.

JASA EXPRESS LETTERS (2023)

添加到收藏夹

Article Acoustics

Approximation of modal wavenumbers and group speeds in an oceanic waveguide using a neural network

A. Varon et al.

Summary: Underwater acoustic propagation is affected by both water column and seabed properties. A Deep Neural Network is employed to predict modal horizontal wavenumbers and group velocities, leading to reduced computational cost while maintaining accuracy. The effectiveness is demonstrated in a simulated Shallow Water 2006 inversion scenario.

JASA EXPRESS LETTERS (2023)

添加到收藏夹

Article Acoustics

Wind turbine noise uncertainty quantification for downwind conditions using metamodeling

Bill Kayser et al.

Summary: The study proposes a method to quantify the uncertainty in wind turbine noise models using quasi-Monte Carlo sampling and metamodeling techniques. By calculating the probability distribution of sound pressure levels, the method provides better knowledge of the statistical characteristics and uncertainties, allowing for improved prediction of wind turbine noise in diverse environments.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2022)

添加到收藏夹

Review Acoustics

A survey of sound source localization with deep learning methods

Pierre-Amaury Grumiaux et al.

Summary: This article presents a survey of deep learning methods for single and multiple sound source localization, specifically focusing on indoor environments with reverberation and diffuse noise. The article provides an extensive overview of the literature on neural network-based sound source localization, organized by neural network architecture, input features, output strategy, data types, and model training strategy. Tables summarizing the literature survey are provided for quick reference.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2022)

添加到收藏夹

Article Acoustics

Gaussian processes for sound field reconstruction

Diego Caviedes-Nozal et al.

Summary: This study explores the use of Gaussian process regression for sound field reconstruction, focusing on spatial correlation to improve accuracy and comparing it to traditional linear regression methods. The results demonstrate the advantages of using GPs in sound field analysis, particularly when prior knowledge is unavailable.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2021)

添加到收藏夹

Article Acoustics

Sequential sparse Bayesian learning for time-varying direction of arrival

Yongsung Park et al.

Summary: This paper introduces methods for estimating time-varying directions of arrival of signals emitted by moving sources using a sparse Bayesian learning framework. Two sequential SBL-based methods are presented to improve DOA estimation performance by propagating statistical information across time. Performance improvements are demonstrated using simulated data and real data from the SWellEx-96 experiment.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Efficient Underwater Acoustical Localization Method Based On Time Difference and Bearing Measurements

Liang Zhang et al.

Summary: This article addresses the underwater acoustical localization problem using TDOA and BAOA measurements, developing a closed-form solution for the hybrid TDOA/BAOA-based source localization problem. An iterative constrained weighted least-squares method is presented to minimize error in the case of large noise. The solution is proven to achieve the CRLB performance, outperforming traditional methods in estimation bias and accuracy, achieving better CRIB performance.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

添加到收藏夹

Article Acoustics

Matched field source localization with Gaussian processes

Zoi-Heleni Michalopoulou et al.

Summary: Gaussian processes can predict a densely sampled field on the array for a sparsely observed acoustic field, with prediction quality depending on the choice of kernel and hyperparameters. When applied to source localization in the ocean with matched-field processing, Gaussian processes lead to denser sampling and denoising, resulting in a higher probability of correct localization compared to conventional processing as noise levels increase.

JASA EXPRESS LETTERS (2021)

添加到收藏夹

Article Management

Parallel Bayesian Global Optimization of Expensive Functions

Jialei Wang et al.

OPERATIONS RESEARCH (2020)

添加到收藏夹

Article Acoustics

A multi-task learning convolutional neural network for source localization in deep ocean

Yining Liu et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2020)

添加到收藏夹

Article Acoustics

Acoustic and geoacoustic inverse problems in randomly perturbed shallow-water environments

Laure Dumaz et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2019)

添加到收藏夹

Article Engineering, Electrical & Electronic

Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

Yuejie Chi et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2019)

添加到收藏夹

Article Acoustics

Machine learning in acoustics: Theory and applications

Michael J. Bianco et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2019)

添加到收藏夹

Article Acoustics

Multi-frequency sparse Bayesian learning for robust matched field processing

Kay L. Gemba et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2017)

添加到收藏夹

Article Acoustics

Multi-frequency sparse Bayesian learning for robust matched field processing Abstracts

[Anonymous]

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2017)

添加到收藏夹

Review Engineering, Electrical & Electronic

Taking the Human Out of the Loop: A Review of Bayesian Optimization

Bobak Shahriari et al.

PROCEEDINGS OF THE IEEE (2016)

添加到收藏夹

Article Computer Science, Information Systems

Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

Niranjan Srinivas et al.

IEEE TRANSACTIONS ON INFORMATION THEORY (2012)

添加到收藏夹

Article Acoustics

Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains

Jan Dettmer et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2012)

添加到收藏夹

Article Acoustics

Trans-dimensional geoacoustic inversion

Jan Dettmer et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2010)

添加到收藏夹

Article Acoustics

Uncertainty estimation in simultaneous Bayesian tracking and environmental inversion

Stan E. Dosso et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2008)

添加到收藏夹

Article Mathematics, Applied

CONSTRUCTING SOBOL' SEQUENCES WITH BETTER TWO-DIMENSIONAL PROJECTIONS

Stephen Joe et al.

SIAM JOURNAL ON SCIENTIFIC COMPUTING (2008)

添加到收藏夹

Article Engineering, Electrical & Electronic

Sparse Bayesian learning for basis selection

DP Wipf et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2004)

添加到收藏夹

Article Acoustics

Quantifying uncertainty in geoacoustic inversion. I. A fast Gibbs sampler approach

SE Dosso

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2002)

添加到收藏夹

Article Acoustics

Quantifying uncertainty in geoacoustic inversion. II. Application to broadband, shallow-water data

SE Dosso et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2002)

添加到收藏夹

Article Acoustics

Quantifying uncertainty in geoacoustic inversion. II. Application to broadband, shallow-water data

Stan E. Dosso et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2002)

添加到收藏夹

Article Acoustics

Spatial resolution of time-reversal arrays in shallow water

S Kim et al.

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2001)

添加到收藏夹

Article Operations Research & Management Science

A taxonomy of global optimization methods based on response surfaces

DR Jones

JOURNAL OF GLOBAL OPTIMIZATION (2001)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.