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SSI-Net: A Multi-Stage Speech Signal Improvement System for ICASSP 2023 SSI Challenge 48

SSI-Net: A Multi-Stage Speech Signal Improvement System for ...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhu, Weixin Wang, Zilin Lin, Jiuxin Zeng, Chang Yu, Tao Tencent Shenzhen China Tsinghua University Shenzhen International Graduate School Shenzhen China Sokendai Kanagawa Japan

ISBN: (纸本)9781728163277

The ICASSP 2023 Speech Signal Improvement (SSI) Challenge concentrates on improving the speech signal quality of real-time communication (RTC) systems. In this paper, we introduce the speech signal improvement network (SSI-Net) submitted to the ICASSP 2023 SSI Challenge, which satisfies the real-time condition. The proposed SSI-Net has a multi-stage architecture. We present the time-domain restoration generative adversarial network (TRGAN) in the first restoration stage for speech restoration. Regarding the second enhancement stage, we employ a lightweight multi-scale temporal frequency convolutional network with axial self-attention (MTFAA-Net) called MTFAA-Lite to enhance the fullband speech. In the subjective test on the SSI Challenge blind test set, our proposed SSI-Net yields a P.835 overall mean opinion score (MOS) of 3.190 and a P.804 overall MOS of 3.178, which eventually takes the 3rd place in tracks 1&2. © 2023 IEEE.

关键词： multi-stage speech signal improvement SSI-Net

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Requirements in PLS, SSML and SRGS Standard-Hindi as a Case Study 3rd

Requirements in PLS, SSML and SRGS Standard-Hindi as a Case ...

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3rd international conference on Smart systems.- Innovations in computing (SSIC)

作者： Verma, Prashant Kumar, Vijay Gupta, Bharat WSI New Delhi India MeitY New Delhi India

ISBN: (纸本)9789811628771;9789811628764

The Speech Synthesis Markup Language (SSML) specification is one of the speech standard of W3C (World-Wide Web Consortium) and is aimed to provide a rich, XML (Extensible Markup Language) for assisting the generation of synthetic speech on Web and other applications. It enables authors a standard way to control various speech parameters in order to improve the quality and give natural voice to the speech synthesis applications such as TTS. Speech Recognition Grammar Specification (SRGS) is a W3C standard that enables developers to define the words and patterns of words that recognized by speech recognizer. W3C PLS (Pronunciation Lexicon Specification) represents the pronunciation information of the words for use in speech recognition and synthesis systems. Currently, these standards enable the output in a large numbers of Latin languages only. Therefore, it is essential to study the existing standards for Indian languages as Indian languages are morphologically rich languages. This paper presents the various aspects of these technologies that need to be identified for Indian languages. The papers proposed the some of the identified requirements/gaps in these speech standards.

关键词： ASR GPS IPA PLS SRGS SSML TTS URI W3C XML

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Optimizing Production and Inventory Management for Defective Items in Manufacturing systems 3

Optimizing Production and Inventory Management for Defective...

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3rd international conference on Intelligent systems. Advanced computing, and Communication, ISACC 2025

作者： Ahmed, Khandakar Rabbi Hossain, Arafat Asif, MD Amaddus Bepary Mohammad, Mahathir Rahaman, Mustafizur Dewan, MD Arafat Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Central Michigan University Dept. of Business Data Analytics Mt Pleasant United States Southern California State University Dept. of Business Administration and Management Los AngelesCA United States Southern California State University Dept. of Business Administration and Management United States Westcliff University Dept. of Cloud Engineering IrvineCA United States Los Angeles City College Dept. of Computer Science Los Angeles United States

ISBN: (纸本)9798331523893

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems. © 2025 IEEE.

关键词： Industry 4.0

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proceedings - 2022 IEEE international conference on Quantum computing and Engineering, QCE 2022

Proceedings - 2022 IEEE International Conference on Quantum ...

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3rd IEEE international conference on Quantum computing and Engineering, QCE 2022

ISBN: (纸本)9781665491136

The proceedings contain 138 papers. The topics discussed include: learning high dimensional multi-response linear models with non-oracular quantum search;collision-free path planning in multi-vehicle deployments – a quantum approach;amplitude amplification for optimization via subdivided phase oracle;incremental data-uploading for full-quantum classification;hybrid quantum-classical neural networks;quantum-classical convolutional neural networks in radiological image classification;quantum representation for sentiment classification;embedding learning in hybrid quantum-classical neural networks;quantum approximate optimization algorithm with sparsified phase operator;quantum robustness verification: a hybrid quantum-classical neural network certification algorithm;estimation of the probabilistic description of quantum physical systems.and characterizing the memory capacity of transmon qubit reservoirs.

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Adaptive user interfaces based on behavioral analysis 3

Adaptive user interfaces based on behavioral analysis

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3rd international conference on Cyber Hygiene and Conflict Management in Global Information Networks, CH and CMiGIN 2024

作者： Artamonov, Yevhen Okhrimenko, Tetyana Golovach, Iurii Krant, Daniil Radchenko, Andrij Radchenko, Kostiantyn Zaloznyi, Taras National Aviation University Liubomyra Huzara Ave. 1 Kyiv03058 Ukraine State Scientific and Research Institute of Cybersecurity Technologies and Information Protection Maksym Zalizniak Str. 3/6 Kyiv03142 Ukraine

The article reveals the possibilities of developing and applying adaptive interfaces that involve analyzing user behavior in e-learning systems. Methods of content adaptation based on personalization are a relevant direction in e-learning systems. because they allow taking into account the needs and requirements of users in an automatic mode. A user model is presented, which includes a comprehensive approach to representing both objective (progress indicators, performance, etc.) and subjective parameters (likes, user feedback, etc.), which allows personalizing the material and interface in accordance with individual requests and capabilities of users. The Levenshtein method determines the similarity between user behavior vectors and predefined learning templates, which allows the system to dynamically adjust data presentation scenarios to support the optimal learning trajectory. This method was used in the MS SQL Server online learning system and in a hardware-software complex for learning Braille. Changing the scenarios for displaying materials in accordance with a specific user category demonstrated significant improvements in learning success rates. Experiments showed that automatic switching of scenarios allowed the system to adapt to the level of learning material mastery by the user, which increased motivation and productivity. Each scenario of displaying educational materials provided for different levels of task complexity and depth of presentation of educational material, so changing scenarios during training added interactivity and increased the level of friendliness of the learning environment, which adapted to the pace and style of each user. System load assessment showed that the Levenstein method with minimal use of hardware resources solved the problem of measuring the similarity between user behavior and learning templates, which allows the system to adapt content in real time, taking into account the current productivity, interest and progress of th

关键词： Digital storage

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Health Information Broadcast distributed Pattern Association based on Estimated Volume 3

Health Information Broadcast Distributed Pattern Association...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Dhanalakshmi, R. Kalpana, A.V. Umamageswaran, J. Kumar, B. Praveen Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India K L University Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665462167

Any study on dataset and inadequate representation are featured in the conventional data mining process. Consequently, data mining findings reveal high chances of challenges and huge root-mean-square approximation errors besides long utilization. The proposed algorithm for associating distribution patterns in streams of medical data is based on density calculations. The distance approach will be utilized to identify any orphaned or questionable data within the data stream, and the gathering of medical data will be used to achieve the aim. Data stream duplicates are compiled by sector match quality, and both the out-of-the-ordinary and the redundant data are eventually eliminated. The density of the information stream is then calculated based on histogram calculations. This investigates the flow of medical information from the viewpoints of concentration and dispersion, as well as the physical characteristics of information distribution, in combination with the results of data density estimate. The model is then built upon a neural network composite, the data distribution parameters in the clustering layer of the model are entered, and in-depth testing is carried out via the neural B.P. network (Back Propagation) on the mining layer of the model. Temporarily, all laws under the grouping of the hidden layer operation value besides the resulting output value are derived, and all rules under the grouping of the hidden layer value besides the conforming contribution value in the medical information stream pattern. The new findings show a curve of contour contiguous to the true curve of the likelihood density, a fair range of medical data dispersion and high medical data consistency, a lower probability of data redundancy, a low RMSEA for mining, a lower time for data recovery. © 2023 IEEE.

关键词： Data mining

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Hierarchical distributed Power Flow Analysis of Multi-TSO-Multi-DSO Power systems

Hierarchical Distributed Power Flow Analysis of Multi-TSO-Mu...

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Energy Engineering and Power systems.(EEPS), international conference on

作者： Yinsheng Su Yingqi Tang Tinghui Zhou Hongyue Zhen Ligang Zhao Changxiang Wang CSG Power Dispatching and Control Center Guangzhou China State Key Laboratory of HVDC Technology CSG Electric Power Research Institute Guangzhou China

This paper proposes a hierarchical distributed power flow (DPF) algorithm for multi-TSO-multi-DSO power systems. The algorithm includes a Ward-equivalence-based multi-TSO DPF at the TSO-TSO level and a single-TSO-multi-DSO DPF at the TSO-DSO level. The algorithm also introduces iteration variable correction and approximate TSO-DSO level solution techniques to improve convergence. The proposed algorithm is designed to effectively improve convergence of DPF algorithm for multiple TSOs and DSOs, reduce power losses, and enhance the stability of power systems. Numerical experiments demonstrate the accuracy, convergence, and efficiency of the proposed algorithm and the convergence improvement techniques.

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3rd international Symposium on New Energy and Electrical Technology, ISNEET 2022

3rd International Symposium on New Energy and Electrical Tec...

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3rd international Symposium on New Energy and Electrical Technology, ISNEET 2022

ISBN: (纸本)9789819905522

The proceedings contain 102 papers. The special focus in this conference is on New Energy and Electrical Technology. The topics include: Novel Design Technique of Fuzzy Adaptive PI Regulator for Permanent Magnet Synchronous Motor;multi-port Energy Router for Virtual Motor Control;bidirectional Communication Between Parallel Buck Converters;identification of Key State Information of Substation Equipment Based on Text Mining and Semantic Analysis Technology;a Simulation Method for the Supply-Demand Conditions of Credits Under Corporate Average Fuel Consumption and New Energy Vehicles Credit Policy;Price Discovery and Volatility Modelling in the EU ETS: Evidence from Phase III;research on Intelligent Energy Management System for Differential Pressure Power Generation;distributed Cooperative Control for DC Microgrid Clusters Interconnected by Multi-port Converter;Quantitative Analysis and Diagnosis of High Resistance Contact Fault Based on ANN Neural Network;real-Time Electromagnetic Transient Simulation for Regional Power Grid Based on Cloudpss;IP Core Design of Phase-Locked Loop for Grid-Connected Photovoltaic Inverter Based on FPGA;Prediction of Minimum Miscibility Pressure (MMP) of CO2-Crude Oil System Based on GWO-RBF Neural Network;Research on Operation Optimization of Crude Oil Pipeline Based on PPSO Algorithm;research on Life Prediction Method of Motor Bearings;research and Application of Power Distribution Monitoring System Based on Edge computing;research on Transformer Inter-turn Fault Protection Method Based on Innovation Graph;research on Relay Protection of Active Distribution Network Based on Innovation Graph;optimal Configuration of Charging Station Based on Multi-objective Genetic Algorithm;Decoupling Control Strategy for Multi-active Bridge DC/DC Converters Based on Dichotomy;preface.

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Blockchain with Sand Cat Swarm Optimized Semantic Attentive Mobilenetv3 Model for Smart IoHT System 3

Blockchain with Sand Cat Swarm Optimized Semantic Attentive ...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Nalluri, Mounika Mupparaju, Chinna Babu Rongali, Aruna Sri Bhuvanesh, A. Murray State University Department of Information Technology Murray United States University of Central Missouri Department of Information Technology MO United States University of the Cumberlands Department of Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350361186

Nowadays, the healthcare (HC) system based on blockchain (BC) assisted internet of things (IoT) has grown a lot in which the medical services are readily accessible to each user via internet sources. However, the HC IoT systems.are highly vulnerable to security threats to render the patient's personal health data. Hence, this study put forth innovative optimized Deep learning (DL) model for BC-assisted secure IoT enabled HC system. The developed study undergoes three major stages namely, medical image encryption, optimal key selection, decryption, and disease classification. Initially, the medical image is encrypted and followed by this, optimal key selection process is performed using a novel sand cat swarm optimizer (SCSO). The encrypted medical data is then stored in the BC and decrypted for further process. Finally, semantic attentive mobileNetV3 (SAtt-MNetV3) model is introduced to extract the features and to classify the decrypted health data to the patients. The simulation is carried out on the Python platform and a publicly available ISIC-2017 database is considered for training process. Various existing studies are also compared with the developed model by computing several performance measures like accuracy, computation time, peak to signal noise ratio (PSNR), recall, sensitivity and specificity metrics. The developed methodology obtained the overall accuracy of 99.2%, recall of 98.9%, specificity of 99.5%, PSNR of 60.7 dB and computation time of 0.13 s. The simulation performances outperform well compared to conventional studies. © 2024 IEEE.

关键词： Cryptography

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HCT-Det: A hybrid CNN-Transformer architecture for 3D object detection from point clouds 3

HCT-Det: A hybrid CNN-Transformer architecture for 3D object...

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Wang, Chao Beijing China

ISBN: (纸本)9781510668522

Detecting 3D objects from LiDAR points is significant for the environmental perception of robotic systems. Some pillarbased 3D object detectors solely use 2D convolutions as feature encoders, which occupy fewer computation resources but sacrifice model accuracy. To activate the potential performance of pillar-based feature representation manners, we propose HCT-Det, a novel hybrid CNN-Transformer architecture for 3D object detection from point clouds. Motivated by the structure re-parameterization technique and vision transformer (ViT) framework, we redesign the 2D backbone and further introduce the Rep-VGG block and multi-head self-Attention (MHSA) mechanism to enrich the scale diversity of feature representation. We perform ablation experiments on the KITTI vision benchmarks to highlight the superiority of our HCT-Det. The evaluation results show that our model outperforms PointPillars baseline, yielding an accuracy of 79.08 moderate AP3D on the car category at a speed of 57.46 FPS on the NVIDIA Tesla P40 platform. Without bells and whistles, our HCT-Det can achieve a reasonable trade-off between accuracy and speed. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

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