Abstract: An evaluation system was established to evaluate efficiently and accurately the sound quality of the interior noise of B-class vehicle in the steady working conditions based on the radial basis function(RBF) neural network.The subjective evaluation tests were carried out for 30 steady noise signal samples with the grade estimation method.The objective parameters having significant effect on the sound quality were extracted by the correlation analysis.An evaluation model was built to predict the sound quality of the vehicle interior noise using an RBF neural network,its average relative predict error is 4.5%.Based on the predict model, using the modular design concept and the multi-threaded parallel processing technique, a virtual instrument based sound quality evaluation system was designed.The field test results showed that compared with traditional subjective evaluation method,the designed system reduces the test time by 90%,and the quality of the evaluation is better.

Key words: vehicle engineering, vehicle interior noise, sound quality evaluation, radial basis function(RBF) neural network, virtual instrument