Classification of vehicle noise comfort level using Probabilistic neural network

Abstract

Determination of vehicle comfort is important because continuous exposure to the noise and vibration leads to health problems for the driver and passengers. In this paper, a vehicle comfort level classification system has been proposed to detect the comfort level in cars using artificial neural network. The database of sound samples from 30 local cars is used. In the stationary condition, the sound pressure level is measured at 1300 RPM, 2000 RPM and 3000 RPM. In the moving condition, the sound is recorded while the car is moving at 30 km/h up to 110 km/h. Subjective test is conducted to find the jury's evaluation for the specific sound sample. The correlation between the subjective and the objective evaluation is also tested. The relationship between the subjective results and the sound metrics is modelled using Probabilistic neural network. It is found from the research that the Spectral Power feature gives the best classification accuracy for both stationary and moving condition model, 86.54% and 82.54% respectively.