• DocumentCode
    3580522
  • Title

    Vehicular Mechanical Condition Determination and On Road Traffic Density Estimation Using Audio Signals

  • Author

    Bhandarkar, Minal ; Waykole, Tejashri

  • Author_Institution
    Dept. of Electron. & Telecommun., Univ. of Pune, Pune, India
  • fYear
    2014
  • Firstpage
    395
  • Lastpage
    401
  • Abstract
    In this paper we are going to estimate the vehicular traffic density by using acoustic or sound signals. Here we will estimate three probable conditions of traffic that is heavy flow traffic (0-10km/h), medium flow (20-40km/h), and free flow (above 40km/h) traffic. Cumulative sound signals consist of various noises coming from various part of vehicles which includes rotational parts, vibrations in the engine, friction between the tires and the road, exhausted parts of vehicles, gears, etc. Noise signals are tire noise, engine noise, engine-idling noise, occasional honks, and air turbulence noise of multiple vehicles. These noise signals contains spectral content which are different from each other, therefore we can determine the different traffic density states and mechanical condition of vehicle. For example, under a free-flowing traffic condition, the vehicles typically move with medium to high speeds and thereby produces mainly tire noise and air turbulence noise. Here we will use SVM and ANN classifiers. In ANN, we are going to use Feed Forword Network.
  • Keywords
    audio signal processing; feedforward neural nets; pattern classification; road traffic; support vector machines; ANN classifiers; SVM classifiers; air turbulence noise; audio signals; engine noise; engine-idling noise; feed forward network; free-flowing traffic condition; occasional honks; road traffic density estimation; sound signals; tire noise; vehicular mechanical condition; Feature extraction; Mel frequency cepstral coefficient; Noise; Support vector machines; Training; Vehicles; Artifitial Neural-Network; Noise signal recognition; Signal processing; Support Vector Machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Communication Networks (CICN), 2014 International Conference on
  • Print_ISBN
    978-1-4799-6928-9
  • Type

    conf

  • DOI
    10.1109/CICN.2014.94
  • Filename
    7065513