Title: Classification of interior noise comfort level of Proton model cars using feedforward neural network
Authors: M.P. Paulraj; Allan Melvin Andrew
Addresses: School of Mechatronics Engineering, University of Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia ' School of Mechatronics Engineering, University of Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
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%.
Keywords: noise comfort levels; subjective evaluation; noise level; frequency band; ANNs; artificial neural networks; artificial intelligence; interior noise; Proton; ride comfort; vehicle noise; sound pressure levels.
International Journal of Artificial Intelligence and Soft Computing, 2013 Vol.3 No.4, pp.344 - 359
Received: 04 Jul 2012
Accepted: 03 Mar 2013
Published online: 12 Jul 2014 *