Heterogeneous multi-classifier for moving vehicle noise classification

Profoundly hearing impaired community (PHIC) cannot moderate wisely an acoustic noise emanated from the moving vehicles in outdoor. They are not able to distinguish either type or distance of the moving vehicles approaching from behind. Therefore, the PHIC encounter risky situation while they are in outdoor. In this paper, a simple system has been proposed to identify the type and distance of a moving vehicle using multi-classifier system (MCS). One-third octave filter band 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 distance of the moving vehicle and the heterogeneous multi-classifier system (HTMCS) based on multilayer Perceptron (MLP), K-nearest neighbor (KNN) and support vector machines (SVM) has been developed. Sampling methods based on repeated holdout (RHO) and bootstrap (BST) methods were used. The developed HTMCS based on the RHO sampling method has yielded better classification accuracy.