Time-Frequency Mask-based Speech Enhancement using Convolutional Generative Adversarial Network

paper, we focus on exploring the relationship between weave patterns and their mechanical properties in woven fiber composites through Machine Learning. Specifically, we explore the interactions between woven architectures and in-plane stiffness properties through Deep Convolutional Neural Network (DCNN) and Generative Adversarial Network (GAN). Our research is important for exploring how woven composite’s pattern is related to its mechanical properties and accelerating woven composite design as well as optimization. We focus on two tasks: (1) Stiffness prediction: Predicting in-plane stiffness properties for given weave patterns. Our DCNN extracts high-level features through several convolutional and fully connected layers to determine the final predictions. (2) Weave pattern prediction: Predicting weave patterns for target stiffness properties, which can be treated as the reverse task of the first one. Due to many-to-one mapping between weave patterns and the composite properties, we utilize a Decoder Neural Network as our baseline model and compare its performance with GAN and Genetic Algorithm. We represent the weave patterns as 2D checkerboard models and use finite element analysis (FEA) to determine in-plane stiffness properties, which serve as input data for our ML framework. We show that: (1) for stiffness prediction, DCNN can predict stiffness values for a given weave pattern with relatively high accuracy (above 93%); (2) for weave pattern prediction, the GAN model gives the best prediction accuracy (above 92%) while Decoder Neural Network has the best time efficiency. HAOTIAN FENG

Generative adversarial networks (GANs) have shown their superiority for speech enhancement. Nevertheless, most previous attempts had convolutional layers as the backbone, which may obscure long-range dependencies across an input sequence due to the convolution operator’s local receptive field. One popular solution is substituting recurrent neural networks (RNNs) for convolutional neural networks, but RNNs are computationally inefficient, caused by the unparallelization of their temporal iterations. To circumvent this limitation, we propose an end-to-end system for speech enhancement by applying the self-attention mechanism to GANs. We aim to achieve a system that is flexible in modeling both long-range and local interactions and can be computationally efficient at the same time. Our work is implemented in three phases: firstly, we apply the stand-alone self-attention layer in speech enhancement GANs. Secondly, we employ locality modeling on the stand-alone self-attention layer. Lastly, we investigate the functionality of the self-attention augmented convolutional speech enhancement GANs. Systematic experiment results indicate that equipped with the stand-alone self-attention layer, the system outperforms baseline systems across classic evaluation criteria with up to 95% fewer parameters. Moreover, locality modeling can be a parameter-free approach for further performance improvement, and self-attention augmentation also overtakes all baseline systems with acceptably increased parameters.

Crop yield estimation and prediction constitutes a key issue in agricultural management, particularly under the context of demographic pressure and climate change. Currently, the main challenge in estimating crop yields based on remotely sensed data and data-driven methods is how to cope with small datasets and the limited amount of annotated samples. In order to cope with small datasets and the limited amount of annotated samples and improve the accuracy of winter wheat yield estimation in the Guanzhong Plain, PR China, this study proposed a method of combining generative adversarial networks (GANs) and convolutional neural network (CNN) for comprehensive growth monitoring of winter wheat, in which the remotely sensed leaf area index (LAI), vegetation temperature condition index (VTCI) and meteorological data at four growth stages of winter wheat during 2012–2017 were generated as the inputs of multi-layer convolutional neural networks (CNNs), and GAN was employed to artificially increase the number of training samples. Then, a linear regression model between the simulated comprehensive growth monitoring (I) and the measured yields was established to estimate yields of winter wheat in the Guanzhong Plain pixel by pixel. The final results showed when GAN was used to double the size of the training samples, and the simulation values obtained by CNN based on augmented samples using GAN provided a better training (R2 = 0.95, RMSE = 0.05), validation (R2 = 0.54, RMSE = 0.16) and testing (R2 = 0.50, RMSE = 0.14) performance than that just using the original samples. The achieved best pixel-scale yield estimation accuracy of winter wheat (R2 = 0.50, RMSE = 591.46 kg/ha) in the Guanzhong Plain. These results showed that small samples can be enlarged by GAN, thus, more important features for reflecting the growth conditions and yields of winter wheat from the remotely sensed indices and meteorological indices can be extracted, and indicated that CNN accompanied with GAN could contribute a lot to the comprehensive growth monitoring and yield estimation of winter wheat and data augmentation methods are extremely useful for the application of small samples in deep learning.

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 secs and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.

Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method.

Brain tumors are formed due to the abnormal growth of the cells that get multiplied and become in uncontrollable perspective. Tumors can damage brain cells by pressing down the skull, which consociate begins in, which negatively affects human health. In advanced stages, a brain tumor is a more dangerous infection that cannot be relieved. Brain tumor detection and early prevention are necessary in today's world. An extreme learning machine (ELM) is a widely adopted algorithm in machine learning. It is proposed to use classification models in brain tumor imaging. This classification is based on the techniques implemented: Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN). CNN efficiently solves the convex optimization problem and is faster, requiring less human effort. The algorithmic architecture of GAN uses two neural networks, pocking one against the other. These networks are implemented in various fields to classify brain Tumor images. The present study mainly aims to introduce a new proposed classification system of preschool children brain imaging with Hybrid Convolutional Neural Networks and with the techniques of GAN. The proposed technique is compared with the existing hybrid-CNN and hybrid-GAN techniques. The outcomes are encouraging because the loss is deduced, and the accuracy facet increases. The proposed system achieved a training accuracy of 97.8% and a validation accuracy of 89%. The outcomes of the studies show that ELM in the platform of GAN for preschool children brain imaging classification has achieved higher predictive performance than the traditional classification mechanisms in increasingly complex scenarios. Time elapsed for training brain images samples finds inference value for training samples and time elapsed value increased by 28.9855%. Approximation ratio for cost based on probability, finding Approximation ratio for low probability range is increased by 88.1%. The combination of CNN, GAN and hybrid-CNN, hybrid-GAN, and hybrid CNN + GAN, compared with the proposed hybrid system, increased Detection latency for low range learning rate by 3.31%.

Hyperspectral remote sensing image classification based on residual generative Adversarial Neural Networks

In recent years, advances in image restoration have prominently featured Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs).Specifically, in seismic image denoising, the DnCNN model, rooted in CNN, employs residual learning combined with batch normalization for a streamlined approach to denoising.Alternatively, SRGAN, leveraging GAN and super-resolution techniques, trains its generative model using a perceptual loss function, accentuating perceptual disparities between generated and authentic images to boost visual quality.Recognizing the importance of preserving salient seismic data, the Constrained-DnCNN model has been introduced, refining seismic data interpretation.Additionally, the CR-SRGAN model, targeting super-resolution of artifacts and color restoration, deviates from conventional training datasets acquired via high-resolution image interpolation and downsampling.Upon comparative analysis, the DCGAN, amalgamating features of CNN and GAN, stands out for harnessing CNN's robust feature extraction.This bolsters the model's capacity to adeptly fit real data distributions.DCGAN's potential spans aspects like multimodal fusion, model interpretability, trustworthiness, and image restoration quality, marking it a promising research avenue.

Deep learning and neural networks have revolutionized artificial intelligence, transforming industries and daily life with applications ranging from voice assistants to medical diagnostics. Despite their ubiquity, these technologies remain enigmatic to many enthusiasts and practitioners. This article demystifies the fundamental concepts underlying neural networks, exploring their biological inspiration, architectural components, and learning mechanisms. Various deep learning architectures are examined, including convolutional neural networks, recurrent neural networks, transformers, and generative adversarial networks, elucidating their distinctive features and applications. The discussion extends to practical considerations in training neural networks, highlighting data requirements, optimization challenges, and regularization techniques. By exploring applications across computer vision, natural language processing, speech recognition, and recommendation systems, the transformative impact of these technologies is illustrated. The article concludes by addressing limitations and ethical considerations, emphasizing the importance of interpretability, fairness, resource efficiency, and environmental sustainability as the field continues to advance.

Self-mixing interferometry (SMI) is superior to other laser interferometry methods due to its simplicity and compactness. However, SMI signals are often complex and difficult to process due to interference in the form of variations in the effective reflectivity of the target, noisy signals, complex signal shapes, and other dependencies. Deep neural networks have been a very popular area of research in computer artificial intelligence in recent years, allowing more implicit features to be uncovered than traditional shallow machine learning. It has been shown that convolutional and back-propagation neural networks can be used for SMI signal processing. There are also studies that have used machine learning genetic algorithms for absolute distance measurement. Based on the above, this study used convolutional neural networks to form a deep neural network for absolute distance measurement based on SMI technology. We first trained the deep convolutional neural network at different feedback strengths. The results of the Convolutional Neural Network (CNN) model showed a coefficient of determination of 0.9987. which is consistent with the required model. The trained network was then used to estimate absolute distances with and without the addition of noise. The comparison proves that the proposed method is noise-proof and has high adaptability for measurements under different conditions.

Background/Objectives: Accurate preoperative characterization of ovarian masses is essential for appropriate clinical management, particularly for borderline ovarian tumors (BOTs), which are less common and often difficult to distinguish from benign or malignant lesions on ultrasound. Although expert subjective ultrasound assessment achieves high diagnostic accuracy, limited availability of highly trained sonologists restricts its widespread application. Artificial intelligence-based approaches offer a potential solution; however, the low prevalence of BOTs restricts the development of robust deep learning models due to severe class imbalance. This study aimed to develop a Convolutional Neural Network (CNN)-based classifier enhanced with Generative Adversarial Networks (GANs) to improve the discrimination of ovarian masses as benign, malignant, or BOT using ultrasound images. Methods: A total of 3816 ultrasound images from 636 ovarian masses were retrospectively analyzed, including 390 benign lesions, 202 malignant tumors, and 44 BOTs. To address class imbalance, a Deep Convolutional GAN (DCGAN) was used to generate 2000 synthetic BOT images for data augmentation. A three-class ensemble CNN model integrating VGG16, ResNet50, and InceptionNetV3 architectures was developed. Performance was assessed on an independent test set and compared with a baseline model trained without DCGAN augmentation. Results: The incorporation of DCGAN-generated BOT images significantly enhanced classification performance. The BOT F1-score increased from 68.4% to 86.5%, while overall accuracy improved from 84.7% to 91.5%. For BOT identification, the final model achieved a sensitivity of 88.2% and specificity of 85.1%. Class-specific AUCs were 0.96 for benign lesions, 0.94 for malignant tumors, and 0.91 for BOTs. Conclusions: DCGAN-based augmentation effectively expands limited ultrasound datasets and improves CNN performance, particularly for BOT detection. This approach demonstrates potential as a decision support tool for preoperative assessment of ovarian masses.

1 - Congruence of deep learning in biomedical engineering: future prospects and challenges

Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are underexplored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc.) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.

Adversarial attacks are a big problem for deep neural networks (DNNs), especially when it comes to image classification tasks where even small changes can lead to big mistakes. This weakness makes deep convolutional neural networks (DCNNs), generative adversarial networks (GANs), and new architectures like Vision Transformers less reliable. In the last few years, there have been a lot of new ways to attack, from gradient-based methods like FGSM and PGD to optimization-based, patch, and backdoor attacks. As a result, defense mechanisms have developed in many areas, such as adversarial training, input transformations, detection frameworks, and generative purification methods. Even with these improvements, no one defense has been shown to be strong enough to work in all situations. Most methods have to choose between accuracy, scalability, and resistance to adaptive adversaries. To solve this problem, researchers are looking into optimization-driven methods to make systems more robust and improve training efficiency. These methods include biologically inspired metaheuristics like Sparrow Search and Cuckoo Optimization, as well as modern deep learning optimizers. Benchmark datasets like MNIST, Fashion-MNIST, and CIFAR-10 have become very important for testing both attacks and defenses. They give us a standard way to measure accuracy, precision, recall, and sensitivity when things go wrong. This review brings together changes from 2018 to 2023, with a focus on optimization-enhanced training and the different types of adversarial strategies and defense paradigms. Moreover, we suggest a hybrid defense framework that combines adversarial training, metaheuristic optimization, and lightweight input filtering to protect against both white-box and black-box attacks at multiple levels. The goal is to get a full picture of the adversarial landscape, find research gaps that keep coming up, and find ways to make deep learning models for image classification that are more resistant to attacks.

Early fault detection (EFD) in run-to-failure processes plays a crucial role in the condition monitoring of modern industrial rotating facilities, which entail increasing demands for safety, energy and ecological savings and efficiency. To enable effective protection measures, the evolving faults have to be recognized and identified as early as possible. The major challenge is to distil discriminative features on the basis of only the ‘health’ signal, which is uniquely available from various possible sensors before damage sets in and before the signatures of incipient damage become obvious and well-understood in the signal. Acoustic emission (AE) signals have been frequently reported to be able to deliver early diagnostic information due to their inherently high sensitivity to the incipient fault activities, highlighting the great potential of the AE technique for EFD, which may outperform the traditional vibration-based analysis in many situations. To date, the ‘feature-based’ multivariate analysis dominates the interpretation of AE waveforms. In this way, the decision-making relies heavily on experts’ knowledge and experience, which is often a weak link in the entire EFD chain. With the advent of artificial intelligence, practitioners seek an intelligent method capable of tackling this challenge. In the present paper, we introduce a versatile approach towards intelligent data analysis adapted to AE signals streaming from the sensors used for the continuous monitoring of rotating machinery. A new architecture with a convolutional generative adversarial network (GAN) is designed to extract the deep information embedded in the AE waveforms. In order to improve the robustness of the proposed EFD framework, a novel ensemble technique referred to as ‘history-state ensemble’ (HSE) is introduced and paired with GAN. The primary merits of HSE are twofold: (1) it does not require extra computing time to obtain the base models, and (2) it does not require a special design of the network architecture and can be applied to different networks. To evaluate the proposed method, a durability rolling contact fatigue test was performed with the use of AE monitoring. The experimental results have demonstrated that the proposed ensemble method largely improves the robustness of GAN.