Sound Quality Evaluation of the Interior Noise of Pure Electric Vehicle Based on Neural Network Model

Ma, Chunfeng; Chen, Chaoyi; Liu, Qinghe; Gao, Haibo; Li, Qing; Gao, Hang; Shen, Yue

doi:10.1109/tie.2017.2711554

Cited by 70 publications

(41 citation statements)

References 14 publications

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“…Recently, many researchers have studied on the activity recognition [21][22][23][24][25]. There are several types of HAR systems.…”

Section: Resultsmentioning

confidence: 99%

Human activity recognition by using convolutional neural network

Kim¹,

Lee²,

Jung³

2019

IJECE

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<p>In recent years, many researchers have studied the HAR (Human Activity Recognition) system. HAR using smart home sensor is based on computing in smart environment, and intelligent surveillance system conducts intensive research on peripheral support life. The previous system studied in some of the activities is a fixed motion and the methodology is less accurate. In this paper, vision-based studies using thermal imaging cameras improve the accuracy of motion recognition in intelligent surveillance systems. We use one of the deep learning architectures widely used in image recognition systems called Convolutional Neural Networks (CNN). Therefore, we use CNN and thermal cameras to provide accuracy and many features through the proposed method.</p>

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“…Recently, many researchers have studied on the activity recognition [21][22][23][24][25]. There are several types of HAR systems.…”

Section: Resultsmentioning

confidence: 99%

Human activity recognition by using convolutional neural network

Kim¹,

Lee²,

Jung³

2019

IJECE

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show abstract

“…Meanwhile, it is possible to design an SQ estimation model by utilizing an ANN to simulate the subjective assessment model in an approximation process of the subjective perception. Many researchers have applied ANNs to SQ evaluation, and they have achieved reliable results [15][16][17][18][19][20]. Steinbach and Altinsoy [15] used ANNs to predict the annoyance evaluations of the pass-by noise of EVs and found that most of the parameters have no linear influence on the annoyance perception.…”

Section: Introductionmentioning

confidence: 99%

Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks

2020

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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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“…Long-term exposure to automobile noise will lead to physical illnesses like tinnitus, dreaminess, palpitation and irritability, hearing loss and even deafness. Therefore, many acoustic models [1][2][3][4][5][6][7][8][9][10] have been developed to measure and predict automobile noise.…”

Section: Introductionmentioning

confidence: 99%

An Automobile Noise Prediction Model Based on Extension Data Mining Algorithm

Lü¹,

Wang²

2019

RIA

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In recent years, the prediction of automobile noise has attracted much attention from the academia. This paper aims to develop an automobile noise prediction model based on basic vehicle parameters and vehicle speed, rather than the features calculated from noise data. First, the extension data mining (EDM) was adopted to preprocess the original data, select the features and establish the matter-element models of automobile noise. Then, the extremum entropy method (EEM), an improved information entropy method, was selected to determine the weight of each feature for the calculation of comprehensive correlation function. To verify its effectiveness of the proposed model, our model was applied to experiments on an automobile noise dataset, in comparison with logistic regression and decision tree. The results show that our model outperformed the contrastive methods in prediction accuracy, eliminating the need to set hyper-parameters in advance. The research findings provide a new method to predict automobile noise based on basic vehicle parameters and vehicle speed.

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Sound Quality Evaluation of the Interior Noise of Pure Electric Vehicle Based on Neural Network Model

Cited by 70 publications

References 14 publications

Human activity recognition by using convolutional neural network

Human activity recognition by using convolutional neural network

Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks

An Automobile Noise Prediction Model Based on Extension Data Mining Algorithm

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