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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.

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.

Reduction of structure-borne noise in the compartment of a car is an important task in automotive engineering. Transfer path analysis using the vibroacoustic reciprocity technique or multiple path decomposition method has generally been used for structure-borne noise path analysis. These methods are useful for solving a particular problem, but they do not quantify the effectiveness of vibration isolation of each isolator of a vehicle. To quantify the effectiveness of vibration isolation, vibrational power flow has been used for a simple isolation system or a laboratory-based isolation system. It is often difficult to apply the vibrational power flow technique to a complex isolation system like a car. In this paper, a simple equation is derived for calculation of the vibrational power flow of an isolation system with multiple isolators such as a car.

This paper presents a new measurement method for the measurement of the short reverberation time of a car compartment. The method is applied to the measurement of reverberation time of a compact size passenger car. The new method is based on the modified wavelet transform for the transient sound signal obtained inside of a car. The measurement error due to a small Bandwidth-Time product (BT) is reduced by using a new method.

This paper presents a new booming index, which is developed by using psychoacoustics theory and neural network theory. The input of neural network is sound metrics for interior noise signal, which replace of the auditory system of a human. The neural network replace of the neuron structure of human' brain. The 150 sounds for the training of neural network or for optimization of the weights of the neuron have been synthesized by using a reference sound signal measured on the drive seat. The correlation for booming sounds between objective values evaluated by the trained neural network system and the subjective values evaluated by the 21 persons are well corresponded.

This paper shows the cause and the solution to the uncommon noise which happens ½ order component of engine rpm when a vehicle with automatic transmission has an air conditioning load and “drive” range load on the engine. By measuring cylinder pressure, main bearing cap vibration, engine mount vibration, and interior noise simultaneously, the cause of the noise can be proved by analyzing and comparing the data. The cause of the uncommon noise is bending vibration of the crank shaft. To solve the problem, one can change the crank shaft dynamics by reducing the mass of the damper pulley.

The weight reduction and noise refinement of powertrain has been major concern in automotive industry although they are known as self trade-off. This paper presents various methods to deal with those problems for new Hyundai 1.5 liter powertrain. It was possible to reduce the weight of powertrain by using plastic for both headcover and intake manifold, aluminum for crankshaft damper pulley and stainless steel for exhaust manifold and by reducing the general thickness of cylinder block On the other hand, the noise refinement of vibration in the powertrain was made by optimizing the engine structure and by adapting the hydraulic lash adjuster valve train system, which was proved to be effective in mechanical noise of engine.

In an automotive engine impulsive sounds and vibration are induced by faults or design constraints which degrade the sound quality of the engine. Thus it is important for an NVH engineer to detect and analyse impulsive sound and vibration signals for both fault diagnosis and also for sound quality assessment. However it is often difficult to detect and identify impulsive signals because of interfering signals such as those due to engine firing, harmonics of crankshaft speed and broadband noise components. These interferences hinder the early detection of faults and improvement of sound quality. In order to overcome this difficulty we present a two-stage ALE (Adaptive Line Enhancer) which is capable of enhancing impulsive signals embedded in background noise. This method is used to pre-process signals prior to time-frequency analysis via a bilinear methods such as the Wigner-Ville distribution and the Choi-Williams distribution.

Rumbling sound is one of the most important sound qualities in a passenger car. In previous work, an objective evaluation method for rumbling sound was developed based on the principal component. In the present paper, the rumbling sound was found to effectively relate not only the loudness but also roughness. The last two subjective parameters are sound metrics in 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 of these interior sounds. Throughout this research, it was found that the results of these evaluations and the output of a neural network have high correlation.

This paper presents practical work on the reduction of gear whine noise. In order to identify the source of the gear whine noise, transfer paths are searched and analyzed by operational deflection shape analysis and experimental modal analysis. It was found that gear whine noise has an air-borne noise path instead of structure-borne noise path. The main sources of air- borne noise were the two global modes caused by the resonance of an axle system. These modes created a vibro-acoustic noise problem. Vibro-acoustic noise can be reduced by controlling the vibration of the noise source. The vibration of noise source is controlled by the modification of structure to avoid the resonance or to reduce the excitation force. In the study, the excitation force of the axle system is attenuated by changing the tooth profile of the hypoid gear. The modification of the tooth profile yields a reduction of transmission error, which is correlated to the gear whine noise.

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.