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Sensors 2017, 17(10), 2323; doi:10.3390/s17102323

An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments

GTM—Grup de recerca en Tecnologies Mèdia, La Salle, Universitat Ramon Llull, Quatre Camins, 30, 08022 Barcelona, Spain
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Received: 6 August 2017 / Revised: 29 September 2017 / Accepted: 10 October 2017 / Published: 12 October 2017
(This article belongs to the Special Issue Sensor Networks for Smart Roads)
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Abstract

One of the main aspects affecting the quality of life of people living in urban and suburban areas is their continued exposure to high Road Traffic Noise (RTN) levels. Until now, noise measurements in cities have been performed by professionals, recording data in certain locations to build a noise map afterwards. However, the deployment of Wireless Acoustic Sensor Networks (WASN) has enabled automatic noise mapping in smart cities. In order to obtain a reliable picture of the RTN levels affecting citizens, Anomalous Noise Events (ANE) unrelated to road traffic should be removed from the noise map computation. To this aim, this paper introduces an Anomalous Noise Event Detector (ANED) designed to differentiate between RTN and ANE in real time within a predefined interval running on the distributed low-cost acoustic sensors of a WASN. The proposed ANED follows a two-class audio event detection and classification approach, instead of multi-class or one-class classification schemes, taking advantage of the collection of representative acoustic data in real-life environments. The experiments conducted within the DYNAMAP project, implemented on ARM-based acoustic sensors, show the feasibility of the proposal both in terms of computational cost and classification performance using standard Mel cepstral coefficients and Gaussian Mixture Models (GMM). The two-class GMM core classifier relatively improves the baseline universal GMM one-class classifier F1 measure by 18.7% and 31.8% for suburban and urban environments, respectively, within the 1-s integration interval. Nevertheless, according to the results, the classification performance of the current ANED implementation still has room for improvement. View Full-Text
Keywords: real-time acoustic event detection; anomalous noise events; road traffic noise; background noise; dynamic noise mapping; wireless acoustic sensor network; binary classification; real-life audio database; urban and suburban environments real-time acoustic event detection; anomalous noise events; road traffic noise; background noise; dynamic noise mapping; wireless acoustic sensor network; binary classification; real-life audio database; urban and suburban environments
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Socoró, J.C.; Alías, F.; Alsina-Pagès, R.M. An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments. Sensors 2017, 17, 2323.

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