A Bayesian method for model selection in environmental noise prediction
A Bayesian method for model selection in environmental noise prediction
Environmental noise prediction and modeling are key factors for addressing a proper planning and management of urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively compared using real noise measurement data collected in different urban areas.
31-42
Martín-Fernández, Laura
9550c944-4c05-44f7-8d66-10af404e87d3
Ruiz, Diego P.
ab9eb00f-171c-417f-8304-5105e41cbd03
Torija, Antonio J.
6dd0d982-fcd6-42b6-9148-211175fd3287
Miguez, Joaquin
7512c330-bb8a-4ba5-8679-10ea7ce09524
March 2016
Martín-Fernández, Laura
9550c944-4c05-44f7-8d66-10af404e87d3
Ruiz, Diego P.
ab9eb00f-171c-417f-8304-5105e41cbd03
Torija, Antonio J.
6dd0d982-fcd6-42b6-9148-211175fd3287
Miguez, Joaquin
7512c330-bb8a-4ba5-8679-10ea7ce09524
Martín-Fernández, Laura, Ruiz, Diego P., Torija, Antonio J. and Miguez, Joaquin
(2016)
A Bayesian method for model selection in environmental noise prediction.
Journal of Environmental Informatics, 27 (1), .
(doi:10.3808/jei.201500295).
Abstract
Environmental noise prediction and modeling are key factors for addressing a proper planning and management of urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively compared using real noise measurement data collected in different urban areas.
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Accepted/In Press date: 20 January 2015
Published date: March 2016
Organisations:
Acoustics Group
Identifiers
Local EPrints ID: 386679
URI: http://eprints.soton.ac.uk/id/eprint/386679
ISSN: 1726-2135
PURE UUID: 68a5cfd2-5a90-4630-a517-b400e3bb8186
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Date deposited: 03 Feb 2016 11:33
Last modified: 14 Mar 2024 22:36
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Contributors
Author:
Laura Martín-Fernández
Author:
Diego P. Ruiz
Author:
Antonio J. Torija
Author:
Joaquin Miguez
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