A framework for community noise modelling using machine learning methods

A framework for community noise modelling using machine learning methods
Title:
A framework for community noise modelling using machine learning methods
Other Titles:
Applied Acoustics
Publication Date:
01 January 2020
Citation:
Wenzu Zhang, Enxiao Liu, Jason C.E. Png, A framework for community noise modelling using machine learning methods, Applied Acoustics, Volume 157, 2020, 107033
Abstract:
A framework for community noise modelling is proposed, where noise sources are treated as their equivalent noise sources defined in a multidimensional space. The noise levels at measurement locations under different settings of noise sources in the space are predicted using a noise propagation simulator and the method of Design of Computer Experiments (DoEs). A surrogate model is built using a Gaussian process for machine learning where the predictions are used as training data. Sound power levels of the equivalent noise sources are obtained by means of measured noise levels and the surrogate model. As a case study, six LAeq-1hr models from 2:30 pm to 8:30 pm of an outdoor food court and shopping area are reported to demonstrate the effectiveness and flexibility of the framework in noise prediction, noise source modelling and its verification.
License type:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Funding Info:
This research / project is supported by the National Research Foundation, Prime Minister’s Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (L2 NIC Award No. L2NICCFP1-2013-6).
Description:
The link to the abstract is available at the publisher's URL: https://doi.org/10.1016/j.apacoust.2019.107033
ISSN:
0003-682X
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