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Audiovisual speech recognition based on a deep convolutional neural network

Data science and Management 2024年第1期7卷 25-34页

作者： Shashidhar Rudregowda Sudarshan Patilkulkarni Vinayakumar Ravi Gururaj H.L. Moez Krichen Department of Electronics and Communication Engineering JSS Science and Technology UniversityMysuru570006India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobar34754Saudi Arabia Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal560064India Department of Information Technology Faculty of Computer Science and Information Technology(FCSIT)Al-Baha UniversityAlaqiq65779-7738Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia

Audiovisual speech recognition is an emerging research *** is the recognition of what someone is saying using visual information,primarily lip *** this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual *** features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural *** feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural ***,integration was performed using a deep convolutional *** audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 *** training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 *** integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

关键词： Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)

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What's the Real: A Novel Design Philosophy for Robust AI-Synthesized Voice Detection 24

What's the Real: A Novel Design Philosophy for Robust AI-Syn...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Hai, Xuan Liu, Xin Tan, Yuan Liu, Gang Li, Song Niu, Weina Zhou, Rui Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Business Data Science Kansai University Osaka Japan

ISBN: (纸本)9798400706868

Voice is one of the most widely used media for information transmission in human society. While high-quality synthetic voices are extensively utilized in various applications, they pose significant risks to content security and trust building. Numerous studies have concentrated on AI-synthesized voice detection to mitigate these risks, with many claiming to achieve promising performance. However, recent research has demonstrated that fake voice detectors suffer from serious overfitting to speaker-irrelative features (SiFs) and cannot be used in real-world scenarios. In this paper, we analyze the limitations of existing fake voice detectors and propose a new design philosophy, guiding the detection model to prioritize learning human voice features rather than the difference between the human voice and the synthetic voice. Based on this philosophy, we propose a novel AI-synthesized voice detection framework named SiFSafer, which uses pre-trained speech representation models to enhance the learning of feature distribution in human voices and the adapter fine-tuning to optimize the performance. The evaluation shows that the average EERs of existing fake voice detectors in the ASVspoof datasets can exceed 20% if the SiFs like silence segments are removed, while SiFSafer achieves an EER of less than 8%, indicating that SiFSafer is robust to SiFs and strongly resistant to existing attacks. © 2024 ACM.

关键词： Fake detection

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Evaluating Naive Bayes Classifiers for Traffic Crash Prediction in Rome, Italy: A Comparative Examination

Evaluating Naive Bayes Classifiers for Traffic Crash Predic...

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International Conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

作者： El Ferouali, Soukaina Elamrani Abou Elassad, Zouhair Abdali, Abdelmounaîm CISIEV Team IT Department Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco SARS Research Team Department of Computer Science ENSAS Cadi Ayyad University Safi Morocco Data Science Laboratory Department of Information Technology Management Daffodil International University Birulia Bangladesh

ISBN: (纸本)9783031714252

It has been observed that technological innovations like Geographic information Systems (GIS), Machine Learning, Artificial Intelligence (AI), Internet of Things (IoT), Big Data, and Intelligent transportation systems can offer useful techniques for identifying and providing details on variables impacting road safety. This is because there is no chance of human error when using these technologies to gather and analyze data. The majority of research on road safety focuses on predicting and averting technological, organizational, and human errors that may cause serious issues or collisions. Traffic accidents are recognized as one of the major global causes of injury and death. Trucks, cars, buses, motorbikes, and pedestrians are involved in crashes that result in almost 3700 fatalities and over 1.3 million deaths. This article aims to examine how well Naive Bayes classifiers perform in the domain of traffic crash prediction for the deceased class. The main objective is to assess and contrast the effectiveness of the three Naive Bayes variations in correctly categorizing occurrences linked to fatalities in traffic accident incidents, which are Bernoulli Naive Bayes, Multinomial Naive Bayes, and Gaussian Naive Bayes. Our study’s findings show that the three Naive Bayes classifiers perform consistently better when classifying traffic accident outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Highway accidents

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Residual and Attention Mechanism-Based Network for Aluminum-Silicon Alloy Microstructure Image Classification 11

Residual and Attention Mechanism-Based Network for Aluminum-...

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11th International Conference on information Systems and Computing Technology, ISCTech 2023

作者： Yang, Xiao Shen, Tao Yin, Jiancheng Liu, Yingli Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Materials Science and Engineering Kunming650093 China

ISBN: (纸本)9798350342406

To enhance the accuracy of aluminum-silicon alloy microstructure image classification, we propose an aluminum-silicon alloy microstructure image classification model based on an improved residual network, incorporating attention mechanisms and multi-scale thinking. The model is built upon the ResNet18 architecture, utilizing enhanced basic residual blocks to select informative features. Additionally, a multi-scale attention module is incorporated into the network to fuse multi-scale features and further extract effective features. Experimental results on an aluminum-silicon alloy microstructure image classification dataset demonstrate that compared to the best-performing model currently available, the proposed model achieves an increase in classification accuracy, precision, recall, and F1 score by 3.17%, 0.74%, 5.53%, and 3.26%, respectively, reaching 94.97%, 96.39%, 93.97%, and 95.17%. These results indicate that the proposed model exhibits strong capabilities in classifying aluminum-silicon alloy microstructure images and can facilitate further research on the properties of aluminum-silicon alloys. © 2023 IEEE.

关键词： Image classification

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On some properties of the bibasic Humbert hypergeometric functions Ξ_(1) and Ξ_(2)

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Applied Mathematics(A Journal of Chinese Universities) 2023年第4期38卷 614-630页

作者： CAI Qing-bo Ghazi S.Khammash Shimaa I.Moustafa Ayman Shehata Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou 362000China Department of Mathematics Al-Aqsa UniversityGazaGaza StripPalestine Department of Mathematics Faculty of ScienceAssiut UniversityAssiut 71516Egypt Department of Mathematics Unaizah College of Science and ArtsUnaizah 56264Qassim UniversityBuraydah 52571QassimSaudi Arabia

The main object of this paper is to deduce the bibasic Humbert functions Ξ_(1) and Ξ_(2)Some interesting results and elementary summations technique that was successfully employed,q-recursion,q-derivatives relations,the q-differential recursion relations,the q-integral representations for Ξ_(1) and Ξ_(2)are *** summation formula derives a list of p-analogues of transformation formulas for bibasic Humbert functions that have been studied,also some hypergeometric functions properties of some new interesting special cases have been given.

关键词： q-calculus bibasic Humbert hypergeometric functions q-derivative

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Solar Observation with the Fourier Transform *** Results of Solar Spectrum near the CO 4.66μm and Mg I 12.32μm

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Research in Astronomy and Astrophysics 2023年第12期23卷 128-137页

作者： Xianyong Bai Zhiwei Feng Zhiyong Zhang Yuanyong Deng Xiaoming Zhu Kaifan Ji Song Feng Xiao Yang Yongliang Song National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Yunnan Observatories Chinese Academy of SciencesKunming 650011China Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and TechnologyKunming 650500China

An infrared solar spectrum observed by ground-based telescopes is seriously affected by the background radiation both from the telescope and sky,relative to the visible *** accuracy is also influenced by the spectral resolution of the Fourier transform *** the paper,we developed a CO_(2)gas cell and installed it in the sample compartment to calibrate the spectral resolution of the Bruker IFS-125HR at infrared *** measured spectral resolution is 0.00342±0.00086 cm^(-1)and 0.0059±0.00024 cm^(-1)at the wavenumbers of798 cm^(-1)and 2136 cm^(-1),*** also updated a fully reflective sunlight feeding system to observe the solar spectrum near CO 4.66μm and Mg I 12.32μ*** quickly pointing the sunlight feeding system about1 degree away from the solar disk center,we are able to measure the background radiation from the telescope and the sky at Huairou Solar Observing *** removing the background radiation,our observed solar spectrum at CO 4.66μm is consistent with that from the National Solar *** Mg I 12.32μm working line selected by the Accurate Infrared Magnetic Field Measurements of the Sun(AIMS)project is also *** method is helpful not only for the spectral resolution calibration and background radiation correction of AIMS but also for other infrared astronomical telescopes.

关键词： Sun infrared-methods observational-instrumentation interferometers

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A Federated Learning Approach for Intrusion Detection System using Deep Neural Network

A Federated Learning Approach for Intrusion Detection System...

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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Tedjini Abdeldjalil Chenine Mustapha Benkaddour Mohammed Kamel Abid Malika Department of Computer Science and Information Technology FNTIC Faculty University of Kasdi Merbah Ouargla Algeria Department of Computer Science and Information Technology Artificial Intelligence and Information Technology Laboratory (LINATI) University of Kasdi Merbah Ouargla Algeria

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

Network technology advancements have significantly increased the risk of device and data compromise, particularly during data sharing and distribution. Machine learning (ML) and deep learning (DL) based intrusion detection systems (IDS) present effective solutions for mitigating these threats. However, in networks with numerous devices, sharing data with a central server for model training poses serious privacy and security risks. Federated Learning (FL) presents a viable solution by eliminating the need to share data with the server for training. Instead, it enables local model training, with only model parameters transmitted to the server for aggregation in a continuous learning process. This paper addresses privacy and security challenges in intrusion detection by leveraging federated learning with Convolutional Neural Networks(CNN). The proposed approach is tested using the UNSW-NB15 dataset under both independent and identically distributed (IID) and non-independent and non-identically distributed (Non-IID) data distributions, providing competitive results compared to centralized architectures, while ensuring data privacy.

关键词： Training Deep learning Data privacy Accuracy Federated learning Network intrusion detection Data models Security Convolutional neural networks Servers

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PROMPTING LABEL EFFICIENCY IN FEDERATED GRAPH LEARNING VIA PERSONALIZED SEMI-SUPERvision 49

PROMPTING LABEL EFFICIENCY IN FEDERATED GRAPH LEARNING VIA P...

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2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024

作者： Mao, Qinghua Lin, Xi Su, Xiu Li, Gaolei Chen, Lixing Li, Jianhua Shanghai Jiao Tong University China Big Data Institute Central South University China School of Computer Science Faculty of Engineering The University of Sydney Australia Shanghai Key Laboratory of Integrated Administration Technologies for Information Security China

ISBN: (纸本)9798350344851

Federated graph learning (FGL) enables the collaborative training of graph neural networks (GNNs) in a distributed manner. A critical challenge in FGL is label deficiency, which becomes more intricate due to non-IID decentralized data. Existing methods have focused on extracting knowledge from abundant unlabeled data, leaving few-shot labeled data unexplored. To this end, we propose ConFGL, a novel FGL framework to enhance label efficiency in federated learning with non-IID subgraphs. We formulate a semi-supervised objective to harness both unlabeled and labeled data, where self-supervised learning is achieved via a graph contrastive module. Additionally, a personalized federated learning (FL) strategy is adopted to concurrently train a global model and an individual model, which helps alleviate the representation disparities encoded by local models. Extensive experiments on four node-level datasets under non-IID settings have shown that ConFGL can consistently provide an average of 4.10% accuracy gains over supervised FL personalization methods while maintaining a higher GPU throughput. © 2024 IEEE

关键词： Graph neural networks

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LUMOS-DM: Landscape-Based Multimodal Scene Retrieval Enhanced by Diffusion Model 30th

LUMOS-DM: Landscape-Based Multimodal Scene Retrieval Enhance...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Huynh, Viet-Tham Nguyen, Trong-Thuan Nguyen, Quang-Thuc Tran, Mai-Khiem Nguyen, Tam V. Tran, Minh-Triet Software Engineering Laboratory and Faculty of Information Technology University of Science VNU-HCM Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science University of Dayton Dayton United States

ISBN: (纸本)9783031533013

information retrieval is vital in our daily lives, with applications ranging from job searches to academic research. In today’s data-driven world, efficient and accurate retrieval systems are crucial. Our research focuses on video data, using a system called LUMOS-DM: Landscape-based Multimodal Scene Retrieval Enhanced by Diffusion Model. This system leverages vision Transformer and Diffusion Models, taking user-generated sketch images and text queries as input to generate images for video retrieval. Initial testing on a dataset of 100 h of global landscape videos achieved an 18.78% at Top-20 accuracy rate and 36.45% at Top-100 accuracy rate. Additionally, video retrieval has various applications, including generating data for advertising and marketing. We use a multi-modal approach, combining sketch and text descriptions to enhance video content retrieval, catering to a wide range of user needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diffusion

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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