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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

In this paper, the relationship was investigated between objective psychoacoustic parameters, A-weighted sound pressure level (SPL) and the results of the subjective evaluation by using grey relational analysis (GRA). The sounds were recorded with eight different passenger cars at four different running conditions. The sound quality indices were calculated, including loudness, sharpness, roughness, fluctuation, and A-weighted SPL. Subjective evaluation was performed by thirty subjects using rating scale method. GRA was compared with traditional correlation analysis, and the comparison shows that some hidden information which could not be found in the traditional correlation analysis was revealed. In order to know the further relationship between fluctuation and subjective evaluation, another subjective evaluation was performed by the same 30 subjects. The result demonstrates that the relationship revealed from GRA is correct.