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
Link To Document :
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