A Comparative Study on Interior Acoustic Comfort Level of Compact Cars using Data Mining Approach
Keywords:Acoustical noise, Automotive, Clustering and classification
Vehicle acoustic comfort is one of the ergonomic measurement criteria that are essential for car occupants. furthermore, interior cabin noise of a car may affect the driver’s concentration when driving. this study is to investigate the noise comfort level of car interior on several compact cars. the objective is to measure interior cabin noise for all three cars and then to compare their acoustic comfort level using subfield data mining approach. a deduction will be made to rate the best car among the three in term of acoustic comfort. the interior cabin noise will be obtained for the cases where engine speed is varied while the cars are in stationary and moving condition. the noise will be assessed according to pre-determined subjective and objective criteria. the sound quality parameters will be assessed by regression analysis. in subjective assessment, the recorded noise is evaluated based on jury assessment. then, the data mining approach is implemented to illustrate the noise level. the collected noise data are divided into five clusters through hierarchical clustering method. to assess the accuracy of noise data clusters, the method of k-nearest neighbours is performed and the results show a high accuracy rate (> 95%). finally, the interior noise of the three cars used is compared by using the analysis of variation. the vehicle acoustic comfort index is produced for the three cars tested in this study. in addition, the acoustic quality among the three cars is presented using anova. annoyance index of the three cars is generated using data mining method. from the results, axia has the best acoustic comfort among of the three cars by objective evaluation. by subjective evaluation, axia recorded the lowest level of annoyance.