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WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets

In addition to air pollution, environmental noise has become one of the major hazards for citizens, being Road Traffic Noise (RTN) as its main source in urban areas. Recently, low-cost Wireless Acoustic Sensor Networks (WASNs) have become an alternative to traditional strategic noise mapping in citi...

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Autores principales: Alías, Francesc, Socoró, Joan Claudi, Alsina-Pagès, Rosa Ma
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506928/
https://www.ncbi.nlm.nih.gov/pubmed/32842527
http://dx.doi.org/10.3390/s20174760
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author Alías, Francesc
Socoró, Joan Claudi
Alsina-Pagès, Rosa Ma
author_facet Alías, Francesc
Socoró, Joan Claudi
Alsina-Pagès, Rosa Ma
author_sort Alías, Francesc
collection PubMed
description In addition to air pollution, environmental noise has become one of the major hazards for citizens, being Road Traffic Noise (RTN) as its main source in urban areas. Recently, low-cost Wireless Acoustic Sensor Networks (WASNs) have become an alternative to traditional strategic noise mapping in cities. In order to monitor RTN solely, WASN-based approaches should automatize the off-line removal of those events unrelated to regular road traffic (e.g., sirens, airplanes, trams, etc.). Within the LIFE DYNAMAP project, 15 urban Anomalous Noise Events (ANEs) were described through an expert-based recording campaign. However, that work only focused on the overall analysis of the events gathered during non-sequential diurnal periods. As a step forward to characterize the temporal and local particularities of urban ANEs in real acoustic environments, this work analyses their distribution between day (06:00–22:00) and night (22:00–06:00) in narrow (1 lane) and wide (more than 1 lane) streets. The study is developed on a manually-labelled 151-h acoustic database obtained from the 24-nodes WASN deployed across DYNAMAP’s Milan pilot area during a weekday and a weekend day. Results confirm the unbalanced nature of the problem (RTN represents 83.5% of the data), while identifying 26 ANE subcategories mainly derived from pedestrians, animals, transports and industry. Their presence depends more significantly on the time period than on the street type, as most events have been observed in the day-time during the weekday, despite being especially present in narrow streets. Moreover, although ANEs show quite similar median durations regardless of time and location in general terms, they usually present higher median signal-to-noise ratios at night, mainly on the weekend, which becomes especially relevant for the WASN-based computation of equivalent RTN levels.
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spelling pubmed-75069282020-09-30 WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets Alías, Francesc Socoró, Joan Claudi Alsina-Pagès, Rosa Ma Sensors (Basel) Article In addition to air pollution, environmental noise has become one of the major hazards for citizens, being Road Traffic Noise (RTN) as its main source in urban areas. Recently, low-cost Wireless Acoustic Sensor Networks (WASNs) have become an alternative to traditional strategic noise mapping in cities. In order to monitor RTN solely, WASN-based approaches should automatize the off-line removal of those events unrelated to regular road traffic (e.g., sirens, airplanes, trams, etc.). Within the LIFE DYNAMAP project, 15 urban Anomalous Noise Events (ANEs) were described through an expert-based recording campaign. However, that work only focused on the overall analysis of the events gathered during non-sequential diurnal periods. As a step forward to characterize the temporal and local particularities of urban ANEs in real acoustic environments, this work analyses their distribution between day (06:00–22:00) and night (22:00–06:00) in narrow (1 lane) and wide (more than 1 lane) streets. The study is developed on a manually-labelled 151-h acoustic database obtained from the 24-nodes WASN deployed across DYNAMAP’s Milan pilot area during a weekday and a weekend day. Results confirm the unbalanced nature of the problem (RTN represents 83.5% of the data), while identifying 26 ANE subcategories mainly derived from pedestrians, animals, transports and industry. Their presence depends more significantly on the time period than on the street type, as most events have been observed in the day-time during the weekday, despite being especially present in narrow streets. Moreover, although ANEs show quite similar median durations regardless of time and location in general terms, they usually present higher median signal-to-noise ratios at night, mainly on the weekend, which becomes especially relevant for the WASN-based computation of equivalent RTN levels. MDPI 2020-08-23 /pmc/articles/PMC7506928/ /pubmed/32842527 http://dx.doi.org/10.3390/s20174760 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
Alías, Francesc
Socoró, Joan Claudi
Alsina-Pagès, Rosa Ma
WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets
title WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets
title_full WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets
title_fullStr WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets
title_full_unstemmed WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets
title_short WASN-Based Day–Night Characterization of Urban Anomalous Noise Events in Narrow and Wide Streets
title_sort wasn-based day–night characterization of urban anomalous noise events in narrow and wide streets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506928/
https://www.ncbi.nlm.nih.gov/pubmed/32842527
http://dx.doi.org/10.3390/s20174760
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