Homogeneous multi-classifier system for moving vehicles noise classification based on multilayer perceptron

Abstract

Abstract

Profoundly hearing-impaired community (PHIC) cannot moderate wisely an acoustic noise emanated from moving vehicle in outdoor environment. Due to this, they have difficulties to distinguish type and the distance of moving vehicles especially the one comes from the rear. Hence, they are at risk whenever they are outdoors. In this paper, a simple system is proposed to identify the type and distance (zone-based) of a moving vehicle using a multi-classifier system (MCS). One-third octave filter bands approach has been used for extracting the significant feature from the noise emanated by the moving vehicle. The extracted features were associated with the type and zone of the moving vehicle and the MCS based on multilayer perceptron has been developed. The developed multilayer perceptron model with the same hidden neuron and training algorithm has been proposed for MCS. This network has been tested for single classifier and MCS. The developed MCS has improved the classification accuracy compared to single classifier.