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Luxury sound is one of the most important sound qualities in a premium passenger car. Previous work has shown that, because of the effects of many different interior sounds, it is difficult to evaluate the luxury sound objectively by using only the A-weighted sound pressure level. In this paper, the characteristics of such sound were first investigated by a systematic approach and a new objective evaluation method for luxury sound-the luxury sound quality index--which was developed by the systematic combination of the seven major interior sound quality indexes based on path analysis. The seven major sounds inside a passenger car were selected by a basic investigation evaluated by the members of a luxury automotive club. Seven major interior sound quality indexes were developed by using sound metrics, which are the psychoacoustic parameters, and the multiple regression method used for the modeling of the correlation between objective and subjective evaluation.

The filtered-x LMS (FX-LMS) algorithm has been applied to the active noise control (ANC) system in an acoustic duct. This algorithm is designed based on the FIR (finite impulse response) filter, but it has a slow convergence problem because of a large number of zero coefficients. In order to improve the convergence performance, the step size of the LMS algorithm was modified from fixed to variable. However, this algorithm is still not suitable for the ANC system of a short acoustic duct since the reference signal is affected by the backward acoustic wave propagated from a secondary source. Therefore, the recursive filtered-u LMS algorithm (FU-LMS) based on infinite impulse response (IIR) is developed by considering the backward acoustic propagation. This algorithm, unfortunately, generally has a stability problem. The stability problem was improved by using an error smoothing filter.

This paper presents a practical real approach for predicting the interior noise caused by the vibration of the powertrain by using the hybrid transfer path analysis (TPA) method. The traditional TPA has been used for the identification of powertrain noise sources. However, with only experimental data for the identification of vibration and noise, it is difficult to determine the effects of modifications to the structure of a powertrain. In order to solve this problem, the vibration of the powertrain in a vehicle is numerically analyzed by using the finite element method (FEM). The vibration of other parts in a vehicle is investigated by using the experimental method based on vibro-acoustic transfer function (VATF) analysis. These two methods are combined for the prediction of interior noise caused by a powertrain.

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.