Classification of acoustic sound signature of moving vehicle using artificial neural network

Abstract

The hearing impaired is afraid of walking along a street and living a life alone. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoor. In this paper, a simple algorithm is proposed to classify the type and distance of the moving vehicles based on the sound signature. A simple experimental protocol is designed to record the vehicle sound under different environment conditions and vehicle speeds. These recorded sound signals are processed and the Discrete Fourier Transformation (DFT) features are extracted. The features are then associated with the corresponding vehicle distance and vehicle type to form the final feature vectors. Two probabilistic neural network (PNN) models are developed to classify the distance and the type of vehicle. The effectiveness of the network is validated through stimulation.