Transmission whine noise quality prediction and weight analysis based on RBF neural network
SHI Quan1，LIU Pei-hai1 ，GUO Dong1 ， YI Peng2
1. Chongqing University of Technology, Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing 400054;
2. Chongqing University of Technology, College of Vehicle Engineering, Chongqing 400054;
3．Chongqing Academy of Science and Technology, Chongqing 401123, P.R.China
Abstract：The method of calculating the impact weight of objective psychoacoustic metrics on subjective evaluation results is proposed using RBF neural network. The gear whine signals were collected in different locations using as the evaluating samples. Subjective sound quality evaluation testing for the 111 noise samples were conducted. Meanwhile, eleven sound quality objective parameters were calculated. By using objective parameters as inputs and subjective values as outputs, a RBF neural network was adopted to establish gear whine sound quality prediction model. The network connection coefficients of the prediction model were used to calculate the impact weight of objective parameters on the results of subjective evaluation. The calculation results show that the SIL-4, the sharpness and loudness over time are the key psychoacoustic parameters to conduct gear whine sound quality.
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