Objective Evaluation for the Passenger Car During Acceleration Based on the Sound Metric and Artificial Neural Network

摘要

While driving a passenger car, a driver can hear many sorts of sounds inside of the car. Among these sounds, booming and rumbling sounds are classified as the dominant sound characteristics of passenger cars. A sound quality index evaluating the quality of these two sounds objectively is therefore required and is developed by using an artificial neural network (ANN) in the present paper. Throughout this research, the booming sound and rumbling sound were found to effectively relate the loudness, sharpness and roughness. The booming sound qualities and rumbling sound qualities for interior sounds were subjectively evaluated by 21 persons for the target of the ANN. After the ANN was trained, the two outputs of this ANN were used for the booming index and rumbling index, respectively. These outputs were tested in the evaluation of the sound quality of the interior sounds which were measured inside of the sixteen passenger cars. Preference rate for the thirty passenger cars was evaluated by using these two developed sound indexes. These indexes were also successfully applied to the enhancement of the interior sound quality for a developmental passenger car.