Multi-resolution DenseNet based acoustic models for reverberant
                        speech recognition

Park, Sunchan; Jeong, Young-Il; Kim, Hyung Soon

doi:10.13064/ksss.2018.10.1.033

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…In order to solve the above problems, this study proposed a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network. e proposed method improves Dense-Net [19] by reducing the number of redundant connections, so as to fully reflect low-level local detail feature information. Finally, QPSO [20] is used to optimize the DenseNet structure and increase the number of hyperparameters, which makes the optimization of network structure more automatic and solves the uncertainty problem of artificial selection.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis

Chen¹,

Zhang²

2021

Scientific Programming

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Due to its own limitations, the traditional teaching quality evaluation method has been unable to adapt to the development of information-based curriculum teaching. Therefore, the establishment of a scientific and intelligent teaching effect evaluation method will help to improve the teaching quality of college teachers. To solve the above problems, a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network is proposed. Firstly, face detection is realized by adding three Haar-like feature blocks and improving the AdaBoost algorithm of a weak classifier connection. Secondly, in order to effectively improve the image imbalance, the MSR algorithm is used to enhance the face data image, which is effectively suitable for network training. Then, by readjusting the connection mode, the DenseNet is improved to fully reflect the local detail feature information of the low level. Finally, quantum particle swarm optimization (QPSO) is used to optimize the DenseNet structure, which makes the optimization of network structure more automatic and solves the uncertainty of manual selection. The experimental results show that the proposed method has a good detection effect and prove the effectiveness and correctness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis

Chen¹,

Zhang²

2021

Scientific Programming

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“…DenseNet is a convolutional neural network with dense connections, which combines the advantages of ResNet and Highway to solve the gradient vanishing problem in deep network (Park et al, 2018 ). The idea of the DenseNet is to ensure the maximum information transfer between the middle and layers of the network, thus it can directly connect all layers.…”

Section: Modified Densenet Architecturementioning

confidence: 99%

A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

Zhang

et al. 2021

Front. Neurorobot.

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With the development of computer vision, high quality images with rich information have great research potential in both daily life and scientific research. However, due to different lighting conditions, surrounding noise and other reasons, the image quality is different, which seriously affects people's discrimination of the information in the image, thus causing unnecessary conflicts and results. Especially in the dark, the images captured by the camera are difficult to identify, and the smart system relies heavily on high-quality input images. The image collected in low-light environment has the characteristic with high noise and color distortion, which makes it difficult to utilize the image and can not fully explore the rich value information of the image. In order to improve the quality of low-light image, this paper proposes a Heterogenous low-light image enhancement method based on DenseNet generative adversarial network. Firstly, the generative network of generative adversarial network is realized by using DenseNet framework. Secondly, the feature map from low light image to normal light image is learned by using the generative adversarial network. Thirdly, the enhancement of low-light image is realized. The experimental results show that, in terms of PSNR, SSIM, NIQE, UQI, NQE and PIQE indexes, compared with the state-of-the-art enhancement algorithms, the values are ideal, the proposed method can improve the image brightness more effectively and reduce the noise of enhanced image.

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“…Moreover, the structural design that enhances feature propagation and feature reuse can greatly reduce the number of parameters. DenseNet has been widely used in semantic cutting [30], speech recognition [31] and image classification [29].…”

Section: Dense Convolutional Networkmentioning

confidence: 99%

“…where x is the input, y is the output and a is a constant between 0 and 1. Finally, based on the researches in [31,33,34], it is shown that deep networks and multi-level connections can improve the performance of algorithm. Therefore, we use RDB instead of Residual Block (RB) which is used in SRGAN as the basic network element.…”

Section: Convmentioning

confidence: 99%

Generative Adversarial Network for Image Super-Resolution Combining Texture Loss

Jiang

2020

Applied Sciences

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Objective: Super-resolution reconstruction is an increasingly important area in computer vision. To alleviate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results, we propose a novel and improved algorithm. Methods: This paper presented TSRGAN (Super-Resolution Generative Adversarial Networks Combining Texture Loss) model which was also based on generative adversarial networks. We redefined the generator network and discriminator network. Firstly, on the network structure, residual dense blocks without excess batch normalization layers were used to form generator network. Visual Geometry Group (VGG)19 network was adopted as the basic framework of discriminator network. Secondly, in the loss function, the weighting of the four loss functions of texture loss, perceptual loss, adversarial loss and content loss was used as the objective function of generator. Texture loss was proposed to encourage local information matching. Perceptual loss was enhanced by employing the features before activation layer to calculate. Adversarial loss was optimized based on WGAN-GP (Wasserstein GAN with Gradient Penalty) theory. Content loss was used to ensure the accuracy of low-frequency information. During the optimization process, the target image information was reconstructed from different angles of high and low frequencies. Results: The experimental results showed that our method made the average Peak Signal to Noise Ratio of reconstructed images reach 27.99 dB and the average Structural Similarity Index reach 0.778 without losing too much speed, which was superior to other comparison algorithms in objective evaluation index. What is more, TSRGAN significantly improved subjective visual evaluations such as brightness information and texture details. We found that it could generate images with more realistic textures and more accurate brightness, which were more in line with human visual evaluation. Conclusions: Our improvements to the network structure could reduce the model’s calculation amount and stabilize the training direction. In addition, the loss function we present for generator could provide stronger supervision for restoring realistic textures and achieving brightness consistency. Experimental results prove the effectiveness and superiority of TSRGAN algorithm.

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Multi-resolution DenseNet based acoustic models for reverberant speech recognition

Cited by 7 publications

References 9 publications

An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis

An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis

A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

Generative Adversarial Network for Image Super-Resolution Combining Texture Loss

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