9540318 Sound quality estimation of car interior engine noise at steady revolutions Ken Kashiwakura (Suzuki Mortor Corporation), Takeo Hashimoto, Shigeko Hatano (Seikei University)

The sound quality (SQ) and sound perception assessments of electric vehicles (EVs) clearly differ from those of conventional internal combustion engine vehicles (ICEVs). Therefore, it is essential to describe and evaluate the SQ of EVs. To evaluate the SQ in EVs, it is necessary to organize evaluators for conducting subjective jury tests, which are time-consuming and labor-intensive. In addition, the evaluation results are subject to the evaluators themselves and other external interferences. With the advancement of machine learning and artificial neural networks (ANNs), this problem can be well solved. This paper outlines a model for SQ estimation in EVs based on a genetic algorithm-optimized back propagation artificial neural network (GA-BP ANN). Moreover, the correlation between the physical-psychoacoustical parameters and the subjective SQ estimations obtained from the jury tests was investigated in this study. It was found that the GA-BP ANN SQ model has many advantages in comparison with the multiple linear regression (MLR) model in terms of precision and generalization. In addition, this method is ready to be applied for rapidly evaluating the SQ in EVs without jury tests, and it can also be of high significance in dealing with the acoustical designs and improvements of EVs in the future.

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Psychoacoustic evaluation of internal combustion engine noises

Automotive and Engine Technology ◽

10.1007/s41104-021-00091-4 ◽

2021 ◽

Author(s):

Sebastian Schneider ◽

Tommy Luft ◽

Hermann Rottengruber

Keyword(s):

Internal Combustion Engine ◽

Empirical Formula ◽

Combustion Engine ◽

Sound Quality ◽

Hearing Tests ◽

Engine Noise ◽

Vehicle Development ◽

Noise Evaluation ◽

Engine Map

AbstractWhen buying a car, the acoustic impression of quality of a vehicle drive train is becoming more and more relevant. The perceived sound quality of the engine unit plays a key role here. Due to the nature of individual background noises, that sound quality is negatively influenced. These noise components, which are perceived as unpleasant, need to be further reduced in the course of vehicle development with the identification and evaluation of disruptive noise components in the overall engine noise being a prerequisite for effective acoustics optimization. In particular, the pulsed ticker noise is classified as particularly annoying in Otto DI engines, which is why this article aims to analyze and evaluate the ticking noise components from the overall noise. For this purpose, an empirical formula was developed which can classify the ticking noise components in terms of their intensity. This is purely perception-based and consists of the impulsiveness, the loudness and the sharpness of the overall engine noise. As with other psychoacoustic evaluation scales, the rating was made from 1 (very ticking) to 10 (not ticking). The ticker noise evaluation formula was then verified on the basis of hearing tests with the help of a jury of experts. According to this, it can be predicted precisely in which engine map areas the ticker noise undermines the pleasantness of the overall engine noise.

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