Show simple item record

dc.contributorUniversitat Ramon Llull. La Salle
dc.contributor.authorSocoró Carrié, Joan Claudi
dc.contributor.authorAlías Pujol, Francesc
dc.contributor.authorAlsina Pagès, Rosa Maria
dc.date.accessioned2020-03-25T16:26:52Z
dc.date.accessioned2023-10-02T06:44:19Z
dc.date.available2020-03-25T16:26:52Z
dc.date.available2023-10-02T06:44:19Z
dc.date.created2017-08
dc.date.issued2017-10
dc.identifier.urihttp://hdl.handle.net/20.500.14342/3443
dc.description.abstractOne 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.eng
dc.format.extent25 p.
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofSensors. 2017, Vol.17, No.10
dc.rights© L'autor/a
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceRECERCAT (Dipòsit de la Recerca de Catalunya)
dc.subject.otherSoroll
dc.subject.otherSoroll urbà
dc.titleAn Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscap
dc.identifier.doihttps://doi.org/10.3390/s17102323
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/LIFE/LIFE13 ENV-IT-001254
dc.relation.projectIDinfo:eu-repo/grantAgreement/SUR del DEC/SGR/2014-SGR-0590
dc.relation.projectIDinfo:eu-repo/grantAgreement/URL i SUR del DEC/Projectes recerca PDI/2017-URL-Proj-013


Files in this item

 

This item appears in the following Collection(s)

Show simple item record

© L'autor/a
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/
Share on TwitterShare on LinkedinShare on FacebookShare on TelegramShare on WhatsappPrint