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SRTNET: Time Domain Speech Enhancement via Stochastic Refinement 48

SRTNET: Time Domain Speech Enhancement via Stochastic Refine...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Qiu, Zhibin Fu, Mengfan Yu, Yinfeng Yin, Lili Sun, Fuchun Huang, Hao Xinjiang University School of Information Science and Engineering Urumqi China Tsinghua University Department of Computer Science and Technology China Xinjiang Key Laboratory of Multi-lingual Information Technology Urumqi China

ISBN: (纸本)9781728163277

Diffusion model, as a new generative model which is very popular in image generation and audio synthesis, is rarely used in speech enhancement. In this paper, we use the diffusion model as a module for stochastic refinement. We propose SRTNet, a novel method for speech enhancement via Stochastic Refinement in complete Time b domain. Specifically, we design a joint network consisting of a deterministic module and a stochastic module, which makes up the "enhance-and-refine"paradigm. We theoretically demonstrate the feasibility of our method and experimentally prove that our method achieves faster training, faster sampling and higher quality. Our code is available at https://***/zhibinQiu/*** © 2023 IEEE.

关键词： diffusion model enhance-and-refine joint training speech enhancement time domain

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Comparison of Feature Extraction Techniques for Ambient Sensor-based In-home Activity Recognition

Comparison of Feature Extraction Techniques for Ambient Sens...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Wang, Aiguo Meng, Yue Zhao, Liang Liu, Jinjun Chen, Guilin School of Electronic Information Engineering Foshan University Foshan China School of Computer Science and Technology Donghua University Shanghai China School of Computer and Information Engineering Chuzhou University Chuzhou China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Ambient sensor-based in-home activity recognition plays a crucial role in the design and development of a smart home to better and actively respond to population aging. From the perspective of machine learning, how to extract features from sensor data largely determines the power of a data-driven human activity recognizer. However, few studies systematically investigate how to encode streaming sensor events. To this end, we herein conduct a comparison of different feature extraction techniques for activity recognition. Specifically, we explore two types of feature representations (i.e., statistical features and structural features) and evaluate their single use and joint use. Besides, we experimentally analyze the impact of window size on prediction accuracy. Finally, we perform experiments on three public datasets with 15 different feature encodings and 6 classifiers. Results show that the joint use of different features generally obtains enhanced accuracy and that the interval 60s of window size achieves a better accuracy-speed tradeoff. © 2022 IEEE.

关键词： Signal encoding

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Effective Density-Based Concept Drift Detection for Evolving Data Streams 24th

Effective Density-Based Concept Drift Detection for Evolving...

引用

24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Cui, Zelin Tian, Hui Shen, Hong Institute of Information Security Engineering Chinese Academy of Sciences Beijing China School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819982103

Concept drift is a common phenomenon appearing in evolving data streams of a wide range of applications including credit card fraud protection, weather forecast, network monitoring, etc. For online data streams it is difficult to determine a proper size of the sliding window for detection of concept drift, making the existing dataset-distance based algorithms not effective in application. In this paper, we propose a novel framework of Density-based Concept Drift Detection (DCDD) for detecting concept drifts in data streams using density-based clustering on a variable-size sliding window through dynamically adjusting the size of the sliding window. Our DCDD uses XGBoost (eXtreme Gradient Boosting) to predict the amount of data in the same concept and adjusts the size of the sliding window dynamically based on the collected information about concept drifting. To detect concept drift between two datasets, DCDD calculates the distance between the datasets using a new detection formula that considers the attribute of time as the weight for old data and calculates the distance between the data in the current sliding window and all data in the current concept rather than between two adjacent windows as used in the exiting work DCDA [2]. This yields an observable improvement on the detection accuracy and a significant improvement on the detection efficiency. Experimental results have shown that our framework detects the concept drift more accurately and efficiently than the existing work. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Imputation of incomplete medical data using missing neighborhood perturbation denoising autoencoder

Imputation of incomplete medical data using missing neighbor...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qi, Huamei Yang, Wenhui Fu, Sen Deng, Lei Central South University Department of Electronic Information Changsha China Ningxia University School of Economics and Management Yinchuan China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

The problem of missing data in the clinical environment is a common one. However, the presence of these missing values or incorrect processing can affect downstream tasks. Imputation is the most effective way to solve the missing problem. Combining deep generative models and multiple imputation idea provides new ideas for the imputation of data, but some methods do not apply to mixed-type tabular data such as clinical data. The missing types of tabular data in real-world scenarios are varied and unpredictable, so it is necessary to construct a generalized imputation method. In this paper, we propose a multiple imputation architecture for missing neighborhood perturbation denoising autoencoder (MI-NPDAE) for imputing mixed types of incomplete medical tabular data for different missing mechanisms. In MI-NPDAE, the neighborhood information of the data is searched to find the best set of donors to be used as the input layer of the network, and the learning of the neighborhood features of the missing locations is ensured by reconstructing new inputs, while the addition of additive noise exposes the model to different missing mechanisms. The experiments were respectively conducted on a public dataset Breast and one lung cancer nutrition dataset from the Chinese Anti-Cancer Society. The experiments on the datasets show that the proposed model shows better results under different missing mechanisms and can maintain a low error on the comparison experiments on data with different missing ratios. In addition, the imputed results also show significant advantages in downstream prediction tasks. © 2024 IEEE.

关键词： Lung cancer

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Intelligent Transportation systems in Smart City: A Systematic Survey

Intelligent Transportation Systems in Smart City: A Systemat...

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2023 International Conference on Robotics and Automation in Industry, ICRAI 2023

作者： Hassan, Muhammad Abul Javed, Roofia Farhatullah Granelli, Fabrizio Gen, Xin Rizwan, Muhammad Ali, Syed Haider Junaid, Hazrat Ullah, Sana University of Trento Department of Information Engineering and Computer Science Trento Italy Kinnarid College for Women Department of Computer Science Lahore Pakistan School of Automation China University of Geosciences Wuhan China College of Engineering and Technology University of Derby Derby United Kingdom University of Engineering and Technology Department of Electrical Engineering Peshawar Pakistan University of Malakand Department of Computer Science and Information Technology Pakistan Qurtuba University of Science and Technology Department of Computer Science Peshawar Pakistan

ISBN: (纸本)9781665464727

The idea of Smart Cities (SCs) was developed to digitize conventional urban living areas and redevelop using digital equipment to enhance lifestyle and resident security. Smart cities cover a wide range of applications such as smart government, smart energy, smart transportation, smart health, and smart education. In this paper, we have examined the mobility-related application e.g., smart transportation. We have focused on related to smart transportation like traffic safety, passenger and driver safety, obstacle detection, accident prevention, crime prevention, and detection, and more importantly smart vehicles like UAVs which played a key role in making these gadgets easy to use in urban environments. For a brief overview, 63 academic publications are examined in the field of ITS and SC technologies. Furthermore, we have provided possible solutions in terms of security in the smart transportation system. © 2023 IEEE.

关键词： Big data

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Feature Selection for Disease Prediction in Medical Diagnosis Using Machine Learning 2

Feature Selection for Disease Prediction in Medical Diagnosi...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Devendran, K. Keerthika, P. Sagana, C. Abinaya, A. Sangeetha, M. Karuppuchamy, R. Bhuvaneshwari, R. Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Dr. N.G.P.Institute of Technology Department of Information Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350368109

Feature selection is the technique widely used when experimented with huge dataset that can be used for dimensionality reduction to extract most recognizable features from the huge dataset and it also improves the performance of classification methods and helps in selecting relevant features. The most common method utilized in this work to extract the important characteristics from the COVID-19 dataset is feature selection, after which the derived dataset is utilized for disease prediction. The outcomes aim to offer nuanced insights into the applicability of these techniques for disease prediction, aiding healthcare professionals and researchers in selecting suitable models tailored to specific scenarios. © 2024 IEEE.

关键词： COVID-19

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GraphFed: A Personalized Subgraph Federated Learning Framework for Non-IID Graphs 20

GraphFed: A Personalized Subgraph Federated Learning Framewo...

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20th IEEE International Conference on Mobile Ad Hoc and Smart systems, MASS 2023

作者： Deng, Pan Liu, Xuefeng Niu, Jianwei Hu, Chunming Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798350324334

Recently, federated graph learning has attracted significant attention, as subgraphs of a global graph may often distribute across different institutions and are subject to privacy restrictions. However, inevitable data heterogeneity among sub-graphs can lead to poor performance of Graph Neural Networks (GNNs) trained by vanilla federated learning algorithm, which has been neglected before. To address this problem we look at subgraphs and find that there are usually overlapping nodes among them. Inspired by the common overlapping nodes among subgraphs, we propose GraphFed, a subgraph federated learning framework which solve the challenge in terms of both data augmentation and personalized federated learning. GraphFed uses common overlapping nodes among subgraphs to locate lost node information and applies data augmentation by retrieving lost nodes and forming common node sets that mitigates data distribution difference. Furthermore, GraphFed treats the overlapping nodes as bridges to calculate the distance among different subgraphs, which implies the similarity of the graph data distribution. Then, we use the distance to perform personalized federated learning. Empirical results and analysis on three real-world graph datasets with graph federated learning settings demonstrate the effectiveness of our proposed framework. © 2023 IEEE.

关键词： Graph neural networks

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Human action recognition in immersive virtual reality based on multi-scale spatio-temporal attention network

Human action recognition in immersive virtual reality based ...

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作者： Xiao, Zhiyong Chen, Yukun Zhou, Xinlei He, Mingwei Liu, Li Yu, Feng Jiang, Minghua School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

Wearable human action recognition (HAR) has practical applications in daily life. However, traditional HAR methods solely focus on identifying user movements, lacking interactivity and user engagement. This paper proposes a novel immersive HAR method called MovPosVR. Virtual reality (VR) technology is employed to create realistic scenes and enhance the user experience. To improve the accuracy of user action recognition in immersive HAR, a multi-scale spatio-temporal attention network (MSSTANet) is proposed. The network combines the convolutional residual squeeze and excitation (CRSE) module with the multi-branch convolution and long short-term memory (MCLSTM) module to extract spatio-temporal features and automatically select relevant features from action signals. Additionally, a multi-head attention with shared linear mechanism (MHASLM) module is designed to facilitate information interaction, further enhancing feature extraction and improving accuracy. The MSSTANet network achieves superior performance, with accuracy rates of 99.33% and 98.83% on the publicly available WISDM and PAMPA2 datasets, respectively, surpassing state-of-the-art networks. Our method showcases the potential to display user actions and position information in a virtual world, enriching user experiences and interactions across diverse application scenarios. © 2024 John Wiley & Sons Ltd.

关键词： human activity recognition multi-scale feature spatio-temporal feature virtual reality

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Attention-Based Recurrent Neural Networks for Medical Image Classification 5th

Attention-Based Recurrent Neural Networks for Medical Image ...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Manjunath, K.V. Alam, Intekhab Nidhya, M.S. Verma, Tushar K. School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Attention-primarily based Recurrent Neural Networks (RNNs) are a technique for medical image classification. RNNs can robotically extract temporal and spatial facts from scientific imaging statistics and make sensible decisions. RNNs are especially nicely-perfect for clinical photograph category obligations, cutting-edge their potential to perceive lengthy-term temporal styles, recognize the relationships between distinct medical picture datasets, and enable In this paper, we overview the existing tries to use interest-based RNNs for medical image classification, discuss the contemporary demanding situations and suggest some ability future guidelines for studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Semantic-Aware Prompt Learning for Multimodal Sarcasm Detection

Semantic-Aware Prompt Learning for Multimodal Sarcasm Detect...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Guangjin Wang, Bao Xu, Fuyong Zhu, Zhenfang Wang, Peipei Wang, Ru Liu, Peiyu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model's "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model's ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： KL divergence multi-view semantic representation multimodal sarcasm detection semantic representation enhancement

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