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AuthorAlmaadeed, N.
AuthorAlmaadeed, Noor
AuthorAsim, Muhammad
AuthorAl-Maadeed, Somaya
AuthorBouridane, Ahmed
AuthorBeghdadi, Azeddine
Available date2019-09-17T09:17:36Z
Publication Date2018-06-06
Publication NameSensors (Switzerland)en_US
Identifierhttp://dx.doi.org/10.3390/s18061858
CitationAlmaadeed, N.; Asim, M.; Al-Maadeed, S.; Bouridane, A.; Beghdadi, A. Automatic Detection and Classification of Audio Events for Road Surveillance Applications. Sensors 2018, 18, 1858.
ISSN1424-8220
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85048310896&origin=inward
URIhttp://hdl.handle.net/10576/11877
Abstract© 2018 by the author. Licensee MDPI, Basel, Switzerland. This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time-domain, frequency-domain, and joint time-frequency features extracted from a class of quadratic time-frequency distributions (QTFDs) to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features.
SponsorFunding text #1 The ability to detect hazardous events on the road can be a matter of life and death, or it can emxpeearnimthenetadtiifofne,regnenceerbateetdwteheenreasunlotsramnadlwliafes raenspdoansliibfelewfoirthwraitminagjothrehpaanpderi.caSp.A..I-Mn .thanisalwyzoerdk,thweerehsualvtse proposedasystemthatcombinest-,f-and(t,f)-domainfeaturestosimultaneouslyconsiderthenon-approach and the results to further improve the quality of the paper. stationary,instantaneous,andlong-termpropertiesofaudiosignalstofacilitateautomaticdetection and classificationofaudio anomalies. The results of experiments performed on a publiclyavailable National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the rbesponsibilityenchmark dofattheaseauthors.t demonstrate the robustness of the proposed method against background noise compared with state-of-the-art approaches for road surveillance applications. Our audio classification system is confirmed to be effective indetecting hazardous...View all Funding text #2 Acknowledgments: This publication was made possible by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the respoDatansibifrliomtyothefthFatalityeauthoAnalysisrs. Reporting System and the General Estimates System; National Highway Traffic Safety Administration: Washington, DC, USA, 2015. 5.ConfEvanco,lictsofInWt.eMre.sThet:ThImpacteauthoofrsRapiddeclareIncidentthattherDetectionearenocononfFrliceewaytsofinAccidentterest. Fatalities. Mitretek: McLean,
Languageen
PublisherMDPI
SubjectCar crashes
SubjectEvent detection
SubjectHazardous events
SubjectTire skidding
SubjectVisual surveillance
TitleAutomatic detection and classification of audio events for road surveillance applications
TypeArticle
Issue Number6
Volume Number18
elsevier.identifier.scopusid SCOPUS_ID:85048310896


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