Pure electric vehicle nonstationary interior sound quality prediction based on deep CNNs with an adaptable learning rate tree

Huang, Haibo; Wu, Jiu Hui; Lim, Teik C.; Yang, Ming; Ding, Weiping

doi:10.1016/j.ymssp.2020.107170

Cited by 24 publications

(17 citation statements)

References 30 publications

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“…The last SoftMax layer and the classified output layer were modified as the fully connected layer with response number 1 and the regression layer, thereby converting the classification network into a regression network. The AlexNet network architecture is shown in Figure 2 [22]. The traditional convolutional neural network requires tens of thousands of training data, but it is time-and labor-consuming to obtain sufficient tagged data.…”

Section: Methodsmentioning

confidence: 99%

“…It adjusts the learning rate adaptively according to the training loss. It was proved that the ALRT convolutional neural network has a better parameter update effect than traditional methods, and its use in future technology is promising [22]. Based on transfer learning, we developed a convolutional neural network model for the degree of noise annoyance under small data sets.…”

Section: Introductionmentioning

confidence: 99%

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Noise Annoyance Prediction of Urban Substation Based on Transfer Learning and Convolutional Neural Network

Fan

et al. 2022

Energies

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The noise pollution caused by urban substations is an increasingly serious problem, as is the issue of local residents being disturbed by substation noise. To accurately assess the degree of noise annoyance caused by substations to surrounding residents, we established a noise annoyance prediction model based on transfer learning and a convolution neural network. Using the model, we took the noise spectrum as the input, the subjective evaluation result as the target output, and the AlexNet network model with a modified output layer and corresponding parameters as the pre-training model. In a fixed learning rate and epoch setting, the influence of different mini-batch size values on the prediction accuracy of the model was compared and analyzed. The results showed that when the mini-batch size was set to 4, 8, 16, and 32, all the data sets had convergence after 90 iterations. The root mean square error (RMSE) of all validation sets was lower than 0.355, and the loss of all validation sets was lower than 0.067. As the mini-batch size increased, the RMSE, loss, and mean absolute error (MAE) of the verification set gradually increased, while the number of iterations and the training duration decreased gradually. In this test, a mini-batch size value of four was appropriate. The resultant convolutional neural network model showed high accuracy and robustness, and the error between the prediction result and the subjective evaluation result was between 2% and 7%. The model comprehensively reflects the objective metrics affecting subjective perception, and accurately describes the subjective perception of urban substation noise on human ears.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise Annoyance Prediction of Urban Substation Based on Transfer Learning and Convolutional Neural Network

Fan

et al. 2022

Energies

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“…We distinguished transitory artefacts using EEGProc and signaled attempts for denial with values more than −90 μV or less than 90 μV. Removal of static and transitory artefacts (computed from step 2 and 3) from the EEG signal in the range of 0.5 Hz to 5 Hz [ 34 , 35 , 36 , 37 ]. …”

Section: Methodsmentioning

confidence: 99%

A Decoding Prediction Model of Flexion and Extension of Left and Right Feet from Electroencephalogram

AlArfaj

Mahmoud

Hafez

2022

Behavioral Sciences

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Detection of limb motor functions utilizing brain signals is a significant technique in the brain signal gain model (BSM) that can be effectively employed in various biomedical applications. Our research presents a novel technique for prediction of feet motor functions by applying a deep learning model with cascading transfer learning technique to use the electroencephalogram (EEG) in the training stage. Our research deduces the electroencephalogram data (EEG) of stroke incidence to propose functioning high-tech interfaces for predicting left and right foot motor functions. This paper presents a transfer learning with several source input domains to serve a target domain with small input size. Transfer learning can reduce the learning curve effectively. The correctness of the presented model is evaluated by the abilities of motor functions in the detection of left and right feet. Extensive experiments were performed and proved that a higher accuracy was reached by the introduced BSM-EEG neural network with transfer learning. The prediction of the model accomplished 97.5% with less CPU time. These accurate results confirm that the BSM-EEG neural model has the ability to predict motor functions for brain-injured stroke therapy.

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“…However, these models usually use psychoacoustics metrics, focusing on predicting the assessment of the subjective perception of the sound quality of a passenger or driver. Many works contribute to noise prediction using artificial intelligence, providing a model for the traffic noise [53][54][55] and sound quality prediction [56][57][58] contexts.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Acoustic Noise Level and Impulsiveness Inside Vehicles in Different Traffic Conditions

Flor

Pena

Oliveira

et al. 2022

Sensors

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Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle under several conditions by comparing measured power levels and two different models for acoustic noise, namely the Gaussian and the alpha-stable distributions. Noise samples were collected in a scenario with real traffic patterns using a measurement setup composed of a Raspberry Pi Board and a microphone strategically positioned. The analysis of the acquired data shows that the observed noise levels are higher when traffic conditions are good. Additionally, the interior noise presented considerable impulsiveness, which tends to be more severe when traffic is slower. Finally, our results suggest that noise sources related to the vehicle itself and its movement are the most relevant ones in the composition of the interior acoustic noise.

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Pure electric vehicle nonstationary interior sound quality prediction based on deep CNNs with an adaptable learning rate tree

Cited by 24 publications

References 30 publications

Noise Annoyance Prediction of Urban Substation Based on Transfer Learning and Convolutional Neural Network

Noise Annoyance Prediction of Urban Substation Based on Transfer Learning and Convolutional Neural Network

A Decoding Prediction Model of Flexion and Extension of Left and Right Feet from Electroencephalogram

Evaluation of Acoustic Noise Level and Impulsiveness Inside Vehicles in Different Traffic Conditions

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