A Bayesian Method for Model Selection in Environmental Noise Prediction

作者：Martin Fernandez L^*; Ruiz D P; Torija A J; Miguez J

来源：Journal of Environmental Informatics, 2016, 27(1): 31-42.

DOI：10.3808/jei.201500295

• environmental noise level prediction
• MAP model selection
• Monte Carlo sampling
• nonlinear state-space model
• particle filtering

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.

• 出版日期2016-3