Classification of interior noise comfort level of Proton model cars using feedforward neural network

Abstract

In this research, a Proton model cars noise comfort level classification system has been developed to detect the noise comfort level in cars using artificial neural network. This research focuses on developing a database consisting of car sound samples measured from different Proton make models in stationary and moving state. In the stationary condition, the sound pressure level is measured at 1,300 RPM, 2,000 RPM and 3,000 RPM while in moving condition, the sound is recorded using dB Orchestra while the car is moving at constant speed from 30 km/h up to 110 km/h. Subjective test is conducted to find the jury's evaluation for the specific sound sample. The feature set is then feed to the neural network model to classify the comfort level. The spectral power feature gives the highest classification accuracy of 88.42%.