Sound quality prediction and weight analysis of vehicles based on GA-BP neural network

This paper carried out a subjective evaluation test with magnitude estimation for 78 noise samples to evaluate the sound quality of vehicles.In the test,six types of B-Class vehicles were taken as the study objects and sound signals collected in co-driver locations at steady states as experimental samples.Meanwhile,seven objective parameters were calculated to describe the sound characteristics.By using objective parameters as inputs,subjective values as outputs,a GA-BP neural network was adopted to establish a sound quality prediction model.Experiments show that the model gives good predictions of high correlation(0.928) and low error(±8%).Then,the network connection coefficients were used to calculate the impact weight of objective parameters on the results of subjective evaluation,and a new model with main parameters was established.As expected,the loudness,sharpness and roughness with a total relative importance of 83% are the most influential parameters in vehicle interior sound quality.