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Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network
Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information ab...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038967/ https://www.ncbi.nlm.nih.gov/pubmed/32046231 http://dx.doi.org/10.3390/s20030903 |
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author | Navarro, Juan M. Martínez-España, Raquel Bueno-Crespo, Andrés Martínez, Ramón Cecilia, José M. |
author_facet | Navarro, Juan M. Martínez-España, Raquel Bueno-Crespo, Andrés Martínez, Ramón Cecilia, José M. |
author_sort | Navarro, Juan M. |
collection | PubMed |
description | Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory. |
format | Online Article Text |
id | pubmed-7038967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70389672020-03-09 Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network Navarro, Juan M. Martínez-España, Raquel Bueno-Crespo, Andrés Martínez, Ramón Cecilia, José M. Sensors (Basel) Article Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory. MDPI 2020-02-07 /pmc/articles/PMC7038967/ /pubmed/32046231 http://dx.doi.org/10.3390/s20030903 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Navarro, Juan M. Martínez-España, Raquel Bueno-Crespo, Andrés Martínez, Ramón Cecilia, José M. Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network |
title | Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network |
title_full | Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network |
title_fullStr | Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network |
title_full_unstemmed | Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network |
title_short | Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network |
title_sort | sound levels forecasting in an acoustic sensor network using a deep neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038967/ https://www.ncbi.nlm.nih.gov/pubmed/32046231 http://dx.doi.org/10.3390/s20030903 |
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