Objective evaluation of the sound quality of the warning sound of electric vehicles with a consideration of the masking effect: Annoyance and detectability

2017 ◽  
Vol 18 (4) ◽  
pp. 699-705 ◽  
Author(s):  
Sang Kwon Lee ◽  
Seung Min Lee ◽  
Taejin Shin ◽  
Manug Han
Keyword(s):  
Electric Vehicles ◽  
Objective Evaluation ◽  
Sound Quality ◽  
Masking Effect

Evaluation of Sound Quality of a Diesel Engine with Considering Masking Effect

2018 ◽  
Vol 28 (6) ◽  
pp. 649-654
Author(s):  
Dong-Un Yun ◽  
Sang-Kwon Lee ◽  
Dongchul Lee ◽  
Insoo Jung
Keyword(s):  
Diesel Engine ◽  
Sound Quality ◽  
Masking Effect

The development of a deep neural network and its application to evaluating the interior sound quality of pure electric vehicles

2019 ◽  
Vol 120 ◽  
pp. 98-116 ◽  
Author(s):  
Hai B. Huang ◽  
Jiu H. Wu ◽  
Xiao R. Huang ◽  
Ming L. Yang ◽  
Wei P. Ding
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Deep Neural Network ◽  
Sound Quality

scholarly journals Optimization of the Sound of Electric Vehicles According to Unpleasantness and Detectability

2019 ◽  
Vol 1 (1) ◽  
pp. 3949-3958
Author(s):  
Jean-François Petiot ◽  
Killian Legeay ◽  
Mathieu Lagrange
Keyword(s):  
Genetic Algorithms ◽  
Electric Vehicles ◽  
Background Noise ◽  
The Other ◽  
Masking Effect ◽  
Sound Design ◽  
Experimental Protocol ◽  
Interactive Genetic Algorithms ◽  
Hearing Test

AbstractElectric Vehicles (EVs) are very quite at low speed, which can be hazardous for pedestrians. It is necessary to add warning sounds but this can represent an annoyance if they are poorly designed. On the other hand, they can be not enough detectable because of the masking effect due to the background noise. In this paper, we propose a method for the design of EV sounds that takes into account in the same time detectability and unpleasantness. It is based on user tests and implements Interactive Genetic Algorithms (IGA) for the optimization of the sounds. Synthesized EV sounds, based on additive synthesis and filtering, are proposed to a set of participants during a hearing test. An experimental protocol is proposed for the assessment of the detectability and the unpleasantness of the EV sounds. After the convergence of the method, sounds obtained with the IGA are compared to different sound design proposals. Results show that the quality of the sounds designed by the IGA method is significantly higher than the design proposals, validating the relevance of the approach.

Intelligent evaluation of the interior sound quality of electric vehicles

2021 ◽  
Vol 173 ◽  
pp. 107684
Author(s):  
Kun Qian ◽  
Zhichao Hou
Keyword(s):  
Electric Vehicles ◽  
Sound Quality

The subjective dimensions of sound quality of standard production electric vehicles

2018 ◽  
Vol 129 ◽  
pp. 354-364 ◽  
Author(s):  
D.J. Swart ◽  
A. Bekker ◽  
J. Bienert
Keyword(s):  
Electric Vehicles ◽  
Sound Quality ◽  
Standard Production

Objective evaluation of the rumbling sound in passenger cars based on an artificial neural network

2005 ◽  
Vol 219 (4) ◽  
pp. 457-469 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Byung-Soo Kim ◽  
Dong-Chul Park
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Objective Evaluation ◽  
Sound Quality ◽  
Passenger Cars ◽  
Passenger Car ◽  
Artificial Neural ◽  
The Relationship

A rumbling sound is one of the most important sound qualities in a passenger car. In previous work, a method for objectively evaluating the rumbling sound was developed based on the principal rumble component. In the present paper, the rumbling sound was found to relate effectively not only to the principal rumble component but also to the loudness and roughness. The last two subjective parameters are sound metrics in psychoacoustics. The 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 the rumbling index and these sound metrics is identified by an artificial neural network. Interior sounds of 14 passenger cars were measured, and 21 passengers subjectively evaluated the rumbling sound qualities of these interior sounds. Through this research, it was found that the results of these evaluations and the output of a neural network have a high correlation. The rumbling index has been successfully applied to the objective evaluation of the rumbling sound quality of mass-produced passenger cars.

scholarly journals Intercultural Differences in the Perception of HVAC Sound Quality in Car Cabins: From Conventional to Electric Vehicles

2021 ◽  
Vol 11 (23) ◽  
pp. 11431
Author(s):  
Massimiliano Masullo ◽  
Katsuya Yamauchi ◽  
Minori Dan ◽  
Federico Cioffi ◽  
Luigi Maffei
Keyword(s):  
Factor Analysis ◽  
Electric Vehicles ◽  
Combustion Engine ◽  
Sound Quality ◽  
Japanese Subject ◽  
Remarkable Difference ◽  
Hvac System ◽  
Flow Rates ◽  
Japanese Group

In electric-powered cars, the production of which is increasing, the HVAC system is responsible for most of the noise inside the car’s cabin, causing significant discomfort for passengers. Moreover, the noise produced by the HVAC affects the perceptible sound inside the car cabin, significantly impacting the perceived quality of the vehicle. It is thus essential to investigate and quantify people’s preferences concerning HVAC noise. Our previous research revealed differences in the HVAC noise between hybrid electric (HEV) and internal combustion engine (ICEV) vehicles. A subsequent factor analysis revealed that the adjectives used to describe the sounds can be grouped into two main dimensions: Aesthetic and Loudness. The present paper highlights the results of a listening test that aimed to identify possible differences in the perception of HVACs’ sound quality between Italian and Japanese subject groups, for ICEV and HEV, in different functioning conditions. Results revealed that the most remarkable difference emerges at high air flow rates, where the Japanese group perceived the quality of sound and annoyance, respectively, to be significantly lower and significantly higher than the Italian group.

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