Light-Weight Self-Attention Augmented Generative Adversarial Networks for Speech Enhancement

In this paper, we propose an improved hybrid approach for text generation that combines the strengths of generative adversarial networks (GANs) and recurrent neural networks (RNNs). The proposed model, named TextGen-GAN, uses a GAN to generate high-level semantic representations of text, which are then passed through an RNN decoder to produce coherent and diverse text. The suggested method produces superior results in terms of both quality and diversity when tested on a number of benchmark datasets. The obtained results show that the combination of GAN and RNN outperforms GAN alone, RNN only, baseline, and state-of-the-art models, demonstrating the effectiveness of the proposed hybrid approach.

Speech Enhancement (SE) system deals with improving the perceptual quality and preserving the speech intelligibility of the noisy mixture. The Time-Frequency (T-F) masking-based SE using the supervised learning algorithm, such as a Deep Neural Network (DNN), has outperformed the traditional SE techniques. However, the notable difference observed between the oracle mask and the predicted mask, motivates us to explore different deep learning architectures. In this paper, we propose to use a Convolutional Neural Network (CNN)-based Generative Adversarial Network (GAN) for inherent mask estimation. GAN takes an advantage of the adversarial optimization, an alternative to the other Maximum Likelihood (ML) optimization-based architectures. We also show the need for supervised T-F mask estimation for effective noise suppression. Experimental results demonstrate that the proposed T-F mask-based SE significantly outperforms the recently proposed end-to-end SEGAN and a GAN-based Pix2Pix architecture. The performance evaluation in terms of both the predicted mask and the objective measures, dictates the improvement in the speech quality, while simultaneously reducing the speech distortion observed in the noisy mixture.

The article presents an analysis of neural networks that can be used to generating web page program code from input image. As part of the research of the possibilities of modification of algorithms for generating code, the method of generating code simultaneous using the generative adversarial and recurrent neural networks based on the input image is proposed. Methods for creating source code using generative adversarial network (GAN) and recurrent neural networks (RNN) are analyzed. Using domain-specific language (DSL) for tree generation code method, it was possible to use GAN and RNN together, achieving on the average 10% productivity increase. Using GRID’s mesh, the generated code is adaptable. To implement the proposed method, software with a graphical user interface is created. Classes of GAN and RNN were considered, among which the feasibility of using a neural network with long-term short-term memory (LSTM) for analysis and forecasting of tags, blocks and HTML elements of the language in the input image was selected and substantiated. The stages of the research are presented. For the experiment datasets made on the basis of existing sites from open sources for 2018–2019 are used. The estimation of the effectiveness of the proposed method is described.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

Personalized medicine represents a paradigm shift in healthcare, aiming to tailor treatment strategies to the unique genetic, environmental, and lifestyle characteristics of individual patients. This approach holds immense potential for improving therapeutic efficacy and minimizing adverse drug reactions. With the rapid advancement of artificial intelligence, deep learning has emerged as a transformative tool in pharmacology, enabling precise modeling of complex biological data and uncovering hidden patterns in patient-specific information. This study investigates the application of deep learning techniques - such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer architectures, and Generative Adversarial Networks (GANs) - in optimizing personalized treatment strategies. Using a diverse dataset comprising electronic health records (EHRs), genomic sequences, and clinical indicators, we developed and trained deep learning models for tasks including drug response prediction, biomarker identification, and adverse drug reaction (ADR) forecasting. Among the models evaluated, Transformer-based architectures demonstrated superior performance, achieving an accuracy of 91.2% and an AUC-ROC of 0.92 in drug response prediction tasks. Moreover, the integration of deep learning models into the treatment pipeline resulted in a 20-30% improvement in drug-patient matching efficiency compared to traditional statistical methods. The findings underscore the potential of AI-powered systems to enhance clinical decision-making and enable precision pharmacotherapy. However, challenges such as data privacy, model interpretability, and regulatory compliance remain critical barriers to widespread adoption. The study also explores future directions, including the implementation of explainable AI (XAI) and federated learning, to address these limitations and facilitate the integration of deep learning into routine clinical practice.

As the need for high-quality video increases it has become necessary to develop smarter compression methods. Artificial neural networks (ANNs) have emerged as an effective tool in this field enabling them to extract the most important features from video and minimize temporal and spatial redundancy resulting in an efficient and high-quality compressed representation. Some of the most prominent models used are: Hybrid neural networks (CNN+RNN(LSTM)) Hybrid neural networks combine convolutional neural networks (CNNs), recurrent neural networks (RNNs), and especially long short-term memory (LSTM) units to extract spatial and temporal features from data, GAN, GANs compress video files while maintaining high perceptual quality. They encode each frame into a compressed latent representation, allowing for realistic reconstruction. This method is efficient, making it ideal for video transmission applications where perceptual accuracy is more important than perfect pixel accuracy. These techniques enable efficient compression without significant quality loss, representing a paradigm shift in modern video compression techniques. After analyzing the results the highest compression ratio (94.75) was achieved by compressing using the GAN algorithm. The highest PSNR (36.08) was achieved by CNN+RNN(LSTM) compression.

Recovering images from highly undersampled measurements has a wide range of applications across imaging sciences. State-of-the-art analytics however are not aware of the image perceptual quality, and demand iterative algorithms that incur significant computational overhead. To sidestep these hurdles, this paper brings forth a novel compressive imaging framework using deep neural networks that approximates a low-dimensional manifold of images using generative adversarial networks. To ensure the images are consistent with the measurements a recurrent GAN (RGAN) architecture is deployed that consists of multiple alternative blocks of generator networks and affine projection, which is then followed by a discriminator network to score the perceptual quality of the generated images. A deep residual network with skip connections is used for the generator, while the discriminator is a multilayer perceptron. Experiments performed with real-world contrast enhanced MRI data corroborate the diagnostic quality of the retrieved images relative to state-of-the-art CS schemes. In addition, it achieves about two-orders of magnitude faster reconstruction.

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

Masked multi-head self-attention for causal speech enhancement

Drug discovery is a multi-stage process, often beginning with the identification of active molecules from a high-throughput screen or machine learning model. Once structure activity relationship trends become well established, identifying new analogs with better properties is important. Synthesizing these new compounds is a logical next step, and is key to research groups that have a synthetic chemistry team or external collaborators. Generative machine learning models have become widely adopted to generate new molecules and explore molecular space, with the goal of discovering novel compounds with desires properties. These generative models have been composed from recurrent neural networks (RNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) and are often combined with transfer learning or scoring of physicochemical properties to steer generative design. While these generative models have proven useful in generating new molecular libraries, often they are not capable of addressing a wide variety of potential problems, and often converge into similar molecular space when combined with a scoring function for desired properties. In addition, generated compounds are often not synthetically feasible, reducing their capabilities outside of virtual composition and limiting their usefulness in real-world scenarios. Here we introduce a suite of automated tools called MegaSyn representing 3 components: a new hill-climb algorithm which makes use of SMILES-based RNN generative models, analog generation software, and retrosynthetic analysis coupled with fragment analysis to score molecules for their synthetic feasibility. We now describe the development and testing of this suite of tools and propose how they might be used to optimize molecules or prioritize promising lead compounds using test case examples.

In the past few years, de novo molecular design has increasingly been using generative models from the emergent field of Deep Learning, proposing novel compounds that are likely to possess desired properties or activities. De novo molecular design finds applications in different fields ranging from drug discovery and materials sciences to biotechnology. A panoply of deep generative models, including architectures as Recurrent Neural Networks, Autoencoders, and Generative Adversarial Networks, can be trained on existing data sets and provide for the generation of novel compounds. Typically, the new compounds follow the same underlying statistical distributions of properties exhibited on the training data set Additionally, different optimization strategies, including transfer learning, Bayesian optimization, reinforcement learning, and conditional generation, can direct the generation process toward desired aims, regarding their biological activities, synthesis processes or chemical features. Given the recent emergence of these technologies and their relevance, this work presents a systematic and critical review on deep generative models and related optimization methods for targeted compound design, and their applications.

Generative adversarial networks (GANs) have been increasingly used as feature mapping functions in speech enhancement, in which the noisy speech features are transformed to the clean ones through the generators. This article proposes a novel speech enhancement model based on a cycle-consistent relativistic GAN with Dilated Residual Networks and a Multi-attention mechanism. Using the adversarial loss, improved cycle-consistency losses, and an identity-mapping loss, a noisy-to-clean generator G and an inverse clean-to-noisy generator F simultaneously learn the forward and backward mappings between the source and target domains. To guarantee the stability of the training process, we replace vanilla GAN loss with relativistic average GAN loss and use spectral normalization in discriminators so that they conform to Lipschitz continuity. Furthermore, we employ two attention-based components as multi-attention mechanism to reduce importing signal distortion: attention U-net gates and dilated residual self-attention blocks. By employing these components, our proposed generators can capture long-term inner dependencies between elements of speech features and further preserve linguistic information. Experimental results on a public dataset indicate that the proposed model achieves state-of-the-art speech enhancement performance, especially in reducing speech distortion and improving signal overall quality. Compared with the representative GAN-based approaches, the proposed method significantly achieves the best performance in terms of STOI, CSIG, COVL, and CBAK objective metrics. Moreover, we demonstrate the contribution of each proposed component including relativistic average loss, attention U-net gate, self-attention layers, spectral normalization, and dilation operation by ten comparison systems.

We introduce a new system for data-driven audio sound model design built around two different neural network architectures, a Generative Adversarial Network (GAN) and a Recurrent Neural Network (RNN), that takes advantage of the unique characteristics of each to achieve the system objectives that neither is capable of addressing alone. The objective of the system is to generate interactively controllable sound models given (a) a range of sounds the model should be able to synthesize, and (b) a specification of the parametric controls for navigating that space of sounds. The range of sounds is defined by a dataset provided by the designer, while the means of navigation is defined by a combination of data labels and the selection of a sub-manifold from the latent space learned by the GAN. Our proposed system takes advantage of the rich latent space of a GAN that consists of sounds that fill out the spaces “between” real data-like sounds. This augmented data from the GAN is then used to train an RNN for its ability to respond immediately and continuously to parameter changes and to generate audio over unlimited periods of time. Furthermore, we develop a self-organizing map technique for “smoothing" the latent space of GAN that results in perceptually smooth interpolation between audio timbres. We validate this process through user studies. The system contributes advances to the state of the art for generative sound model design that include system configuration and components for improving interpolation and the expansion of audio modeling capabilities beyond musical pitch and percussive instrument sounds into the more complex space of audio textures.KeywordsGenerative sound modelingParameter mappingAudio texturesNeural networks

Deep Neural Networks (DNNs) have been successfully adopted as a regression model in speech enhancement. However, the performance in the battlefield environment is not always satisfactory because the noise energy is often dominating in certain speech segments causing speech distortion. For the speech enhancement in complicated battlefield environment where multiple noises can simultaneously corrupt speech, such as gunshots and explosions, we propose an enhanced method to improve the existing DNN-based speech enhancement by using Recurrent Neural Networks (RNNs). This RNN model judges whether each frame is in a low SNR state, and then fuses two DNN-based speech enhancement models. The proposed method is compared with existing DNN-based speech enhancement techniques through the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) scores in various noisy speech conditions. The experimental results demonstrate significant improvements over the state-of-the-art techniques and reflect the usability of the method in a real battlefield environment.KeywordsSpeech enhancementDeep Neural Networks (DNNs)Recurrent Neural Networks (RNNs)Battlefield environment

As a pre-processing technology in other speech applications, speech enhancement technology is one of the kernel technologies in the field of information science. Recent research has found that generative adversarial networks have achieved a lot of research results in the field of speech enhancement. But generative adversarial networks are more difficult to train and stabilize in its application to speech enhancement. In this paper, we proposed a speech enhancement method based on relativistic Wasserstein generative adversarial networks. The experimental results show that it provides more stable training results, speeds up the convergence of the network, and produces a better generative model to improve speech enhancement without increasing the computational effort. The model provides better performance in many methods of comparing assessment metrics.