Classification of interior noise comfort level of proton model cars using artificial neural network

Vehicle Noise Comfort Index (VNCI) has been developed recently to evaluate the sound characteristics of passenger cars. VNCI indicates the interior vehicle noise comfort using a numeric scale from 1 to 10. 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 feedforwardtrained by backpropagation algorithm, Elman and Probabilistic neural network.

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