Use of back-propagation neural networks to predict both level and temporal-spectral composition of sound pressure in urban sound environments

Torija Martinez, AJ; Ruiz, DP; Ramos-Ridao, A

Abstract

One of the main challenges of urban planning is to create soundscapes capable of providing inhabitants
with a high quality of life. Urban planners need tools that enable them to approach the final goal of
designing, planning, and assessing soundscapes in order to adapt them to the needs of the population.
Nowadays, authorities have models for predicting the A-weighted equivalent sound-pressure level (LAeq).
Nevertheless, it is necessary to analyze not only the (LAeq) parameter but also the temporal and spectral
composition of the sound pressure in the soundscape considered. The problem of modelling and predicting
environmental noise in urban settings is a complex and non-linear problem. Therefore, in the
present study, a prediction model based on a back-propagation neural network to solve this problem is
proposed and examined. This model (STACO model) is intended to predict the short-term (5-min integration
period) level and temporal-spectral composition of the sound pressure of urban sonic environments.
Here, it is shown that the proposed model yields a precise and accurate prediction. Moreover, the
results in this work demonstrate the validity of generalization of the STACO model, being applicable not
only for the situations/locations measured, but also for any situation/location of a medium-sized urban
setting, with some prior adjustment. In summary, the prediction model proposed in this study may serve
as a tool for the integration of acoustical variables in city planning.

Citation

Torija Martinez, A., Ruiz, D., & Ramos-Ridao, A. (2012). Use of back-propagation neural networks to predict both level and temporal-spectral composition of sound pressure in urban sound environments. Building and Environment, 52, 45-56. https://doi.org/10.1016/j.buildenv.2011.12.024

• Journal Article Type: Article
• Acceptance Date: Dec 29, 2011
• Online Publication Date: Jan 5, 2012
• Publication Date: Jan 5, 2012
• Deposit Date: Dec 2, 2019
• Journal: Building and Environment
• Print ISSN: 0360-1323
• Publisher: Elsevier
• Volume: 52
• Pages: 45-56
• DOI: https://doi.org/10.1016/j.buildenv.2011.12.024
• Publisher URL: https://doi.org/10.1016/j.buildenv.2011.12.024
• Related Public URLs: https://www.sciencedirect.com/journal/building-and-environment
• Additional Information: Funders : Consejería de Innovación, Ciencia y Empresa de la Junta de Andalucía; Grant Number: P07-TIC-03269