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Development of Sound Index for Refinement of Rumbling Noise of a Passenger Car Using Artificial Neural Network Based on Human
Published May 23, 2004 by Society of Automotive Engineers of Korea in South Korea
Rumbling sound is one of the most important sound qualities in a passenger car. In the previous work, objective evaluation method for rumbling sound has been developed based on the principal rumble component. In the present paper, the rumbling sound was found to effectively relate to not only principal rumble component but also loudness and roughness. Last two subjective parameters are sound metrics in the psychoacoustics. Principal rumble component, roughness and loudness were used as the sound metrics for the development of the rumbling index to evaluate the rumbling sound objectively. The relationship between rumbling index and these sound metrics is identified by an artificial neural network (ANN). Interior sounds of 14 passenger cars were measured, and 21 persons subjectively evaluated the rumbling sound qualities for these interior sounds. Throughout this research, it was found that results of these evaluations and the output of a neural network have high correlation. The rumbling index has been successfully applied to the objective evaluation of the rumbling sound quality of mass-produced passenger cars.