Study on the prediction of car acceleration noise sound quality based on
support vector machine
ZHOU Minggang， LIU Yang， CHEN Yuan， WEN Yao
（Engineering Design and Research Institute of Agricultural Machinery，
Hubei University of Technology，Wuhan 430068，China）
Abstract: To accurately evaluate the sound quality of car interior accelerating noise， the noise samples from 14 different brands of cars during acceleration from 30 km/h to 80 km/h were selected， and subjective evaluation test was carried out with the paired comparison method， meanwhile， five main psychoacoustic parameters were calculated， and the objective parameters having significant effect on the sound quality were extracted by the correlation analysis. A car interior accelerating sound quality prediction model was built based on support vector machine （SVM）， and the relative predict error had been proved less than 9.5% these shows that the prediction model can accurately predict the sound quality of the car interior noise.
Keywords: sound quality； accelerating condition； support vector machine； prediction model