Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Sun, Cunwei; Yang, Yuxin; Wen, Chang; Xie, Kai; Wen, Fangqing

doi:10.3390/s18072399

Cited by 38 publications

(23 citation statements)

References 32 publications

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“…Deep learning based methods are already being used in various fields [17][18][19][20][21]. Different authors have already proposed several biomedical image detection techniques.…”

Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

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“…Deep learning based methods are already being used in various fields [17][18][19][20][21]. Different authors have already proposed several biomedical image detection techniques.…”

Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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“…We start training with generators ( g ) and discriminators ( d ) with low spatial resolution, with a resolution of 4 × 4 at the beginning, and then add a convolution layer to G and D after each training, thus as to gradually improve the spatial resolution of the generated signal [ 32 , 33 , 34 , 35 ]. All involved convolution layers can be retrained in the whole training process.…”

Section: Weak Signal Reconstruction Methods Under Emdnn Modelmentioning

confidence: 99%

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Chen

Xie

Wen

et al. 2020

Sensors

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In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.

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“…Transfer learning (TL) is an improvement for CNN, which transfers the pre-trained experience from the source domain to the target domain so that the CNN model can possess a better image recognition ability or deal with a new objective that has few labeled images [28,58,59]. It has been proven that TL forms of CNN have good generalization, and compared to the result of prototypes, CNN-TL models have a stronger image feature extraction ability outside the range of the training data [53,60,61].…”

Section: Transfer Learning Methodsmentioning

confidence: 99%

Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

et al. 2019

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Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

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Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Cited by 38 publications

References 32 publications

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

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