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Vehicle speed estimation using audio features and neural networks

dc.creatorGiraldo-Guzman J.
dc.creatorMarrugo A.G.
dc.creatorContreras-Ortiz S.H.
dc.date.accessioned2020-03-26T16:32:38Z
dc.date.available2020-03-26T16:32:38Z
dc.date.issued2017
dc.identifier.citationProceedings of the 2016 IEEE ANDESCON, ANDESCON 2016
dc.identifier.isbn9781509025312
dc.identifier.urihttps://hdl.handle.net/20.500.12585/8940
dc.description.abstractMany car accidents that result in pedestrian deaths or serious injuries are due to their inattention when crossing the street. Pedestrians often get distracted using mobile phones or music players, what prevents them to perceive warning signs and sounds. In this work, we developed a method to estimate the speed of an approaching vehicle using features of the generated acoustic signals. This system can be used as a component of a warning system of potential road risks for pedestrians. We used a single microphone to record audio signals. They were processed to extract features in frequency and time domains that were used as inputs to a neural network. Speed estimation was done using a feed forward neural network. We used several architectures and training algorithms. Results show mean error percentages of 14.57% for speeds from 10 to 40 km/h when using a neural network with two hidden layers. © 2016 IEEE.eng
dc.description.sponsorshipIEEE Peru Section
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85015226955&doi=10.1109%2fANDESCON.2016.7836250&partnerID=40&md5=f9ffd2bccf30cac1571f2e65300bc7b5
dc.sourceScopus2-s2.0-85015226955
dc.titleVehicle speed estimation using audio features and neural networks
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datacite.rightshttp://purl.org/coar/access_right/c_16ec
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94f
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.source.event2016 IEEE ANDESCON, ANDESCON 2016
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1109/ANDESCON.2016.7836250
dc.subject.keywordsAccidents
dc.subject.keywordsAcoustic signals
dc.subject.keywordsAudio features
dc.subject.keywordsFrequency and time domains
dc.subject.keywordsHidden layers
dc.subject.keywordsMusic players
dc.subject.keywordsSerious injuries
dc.subject.keywordsSpeed estimation
dc.subject.keywordsTraining algorithms
dc.subject.keywordsPedestrian safety
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.relation.conferencedate19 October 2016 through 21 October 2016
dc.type.spaConferencia
dc.identifier.orcid56520286300
dc.identifier.orcid24329839300
dc.identifier.orcid57210822856


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