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共 3228 条记录，以下是11-20 订阅

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Deep Learning for Speech Separation: A Comprehensive Review 3rd

Deep Learning for Speech Separation: A Comprehensive Review

引用

3rd International conference on Advances in information and communication Technology, ICTA 2024

作者： Thi, Duyen Nguyen Tan, Ha Minh Phung, Trung-Nghia Vu, Duc-Quang Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh City Viet Nam Department of Computer Science and Information Systems Thai Nguyen University of Education Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

Integrating deep learning in speech separation has revolutionized audio signal processing, impacting fields like speech recognition, audio-visual content creation, telecommunication, hearing aid technologies, etc. In time-frequency domain separation, models leverage amplitude and phase information while the waveform is used for training in the time-domain speech separation model. An emerging approach uses complex values, capturing intricate audio nuances for improved performance. Modern models prioritize speaker-independent separation, adapting to dynamic or unknown speaker identities. Their ability to simultaneously separate multiple speakers is crucial for applications like conference calls, overlapping dialogues, and surveillance. This review explores deep models in speech separation, offering insights into the time domain, and time-frequency domain strategies. We outline the current state of models and state-of-the-art learning methods for audio segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Frequency domain analysis

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Optimization of UWB-Based Indoor Positioning Algorithm and Design of Positioning System 24

Optimization of UWB-Based Indoor Positioning Algorithm and D...

引用

4th International conference on Signal processing and communication Technology, SPCT 2024

作者： Hu, Yingkun Zhuang, Yihui Zhang, Yingbin Zhang, Dongbo School of Computer and Information Engineering Guangdong Polytechnic of Industry & Commerce Guangdong Guangzhou China

ISBN: (纸本)9798400710636

Considering the varying advantages, disadvantages, and implementation difficulties of current indoor positioning algorithms, this paper conducts a comparative analysis of common UWB ranging methods. The Two-Way Ranging (TWR) algorithm significantly improves ranging accuracy compared to the One-Way Ranging (OWR) approach. An optimized bi-directional TWR algorithm is proposed, in which the tag only needs to send a single Poll message, reducing ranging time and tag power consumption. To address measurement errors, where the trilateration circles may not intersect at a single point, the least squares method is employed to linearize and optimize the equations. A real-time indoor positioning system is designed, incorporating the optimized bi-directional TWR algorithm and trilateration algorithm. The software workflows for the tags and base stations are developed, and the system visualizes the distances from the tag to four base stations in the upper computer software, along with the derived position coordinates (X, Y, Z). The system achieves static errors within 0.05–0.06 meters, demonstrating its high accuracy for indoor real-time positioning. © 2024 Copyright held by the owner/author(s).

关键词： Least squares approximations

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Semantic Clustering of Image Retrieval Databases used for visual Localization

Semantic Clustering of Image Retrieval Databases used for Vi...

引用

2025 IEEE/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Hölzemann, Henry Fiolka, Torsten Fraunhofer Institute for Communication Information Processing and Ergonomics Fkie Germany

ISBN: (纸本)9798331510831

Accurate self-localization of unmanned aerial systems (UAS) is needed to reduce their dependency on global navi-gation satellite systems (GNSS). Image retrieval techniques comparing aerial images with a reference database can be used for visual localization (VL). But the search space may be vast and a full search not feasible on a small UAS. In this work, we propose a novel solution that divides the reference database into smaller clusters based on the semantic con-tent of images. To this end, we generate and make use of a dataset for semantic segmentation of aerial image captures. By characterizing scenes and objects in images semanti-cally, retrieval-based systems are able to differentiate images and scenes efficiently. Using a divide-and-conquer approach, images with similar semantics are matched within smaller partial databases. This technique leads to reduced search times and approaches VL as a feasible solution for UAS localization in large-scale outdoor environments. © 2025 IEEE.

关键词： Semantic Segmentation

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Research on automated annotation technology for radar range-angle-doppler data based on visual supervision mechanism 4

Research on automated annotation technology for radar range-...

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4th International conference on Electronic information and communication Engineering, EICE 2025

作者： Miao, Jilong Wang, Ping Wang, Xinhong Liu, Fuqiang School of Electronic and Information Engineering Tongji University Shanghai200092 China

With the development of autonomous driving technology, vehicles integrate camera, lidar, and radar modules for environment perception. In mass-produced models, cameras and lidars mostly serve as primary sensors, while radar is mainly limited to distance detection and collision prevention. Given radar's unique working principle, it potentially offers stronger sensing capabilities. However, its signals are hard to process and annotate, and relevant research lacks sufficient dataset support. This study presents a method for processing radar raw signals and proposes an image-supervised automatic annotation approach for radar tensor data in the Range-Angle-Doppler (RAD) format. Meanwhile, we introduce the RADaSet dataset containing synchronized camera, lidar, and radar data, with the radar's RAD data annotated via the self-annotation method. © 2025 Institute of Physics Publishing. All rights reserved.

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Design and Implementation of Overseas communication Path for Chinese Corporate Image Based on Multimodal Discourse Analysis with Machine Learning Algorithm 24

Design and Implementation of Overseas Communication Path for...

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3rd International conference on Artificial Intelligence and Intelligent information processing, AIIIP 2024

作者： Lin, Jiling Lei, Fei Liu, Weiliang School of Foreign languages Guangdong University of Science & Technology Guangdong Dongguan China School of Finance and Economics Guangdong University of Science & Technology Guangdong Dongguan China Huizhou Modern Vocational Technology School Guangdong Huizhou China

ISBN: (纸本)9798400707308

In an increasingly globalized economy, the communication of corporate image plays a pivotal role in shaping perceptions and fostering relationships with international stakeholders. This study explores the design and implementation of effective overseas communication strategies for Chinese corporations, leveraging multimodal discourse analysis and machine learning algorithms. By synthesizing quantitative and qualitative data, we analyze various communication modalities—including visual, textual, and auditory elements—utilized in corporate branding across different cultural contexts. Employing machine learning techniques, we identify patterns and insights from vast datasets, enabling a nuanced understanding of how multimodal elements influence audience reception and engagement. Our findings reveal key factors that contribute to successful corporate image dissemination and highlight the importance of cultural sensitivity in communication strategies. The research provides a framework for Chinese corporations to enhance their global presence by integrating data-driven insights into their communication practices, ultimately fostering a more favorable corporate image and stronger international relationships. This study not only contributes to the field of corporate communication but also offers practical implications for practitioners seeking to navigate the complexities of multicultural environments in an era of rapid technological advancement. © 2024 Copyright held by the owner/author(s).

关键词： Machine Learning Algorithms

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GRU Based Multiview Summarisation to obtain fast effective Key-frame

GRU Based Multiview Summarisation to obtain fast effective K...

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2025 International conference on Computer, Electrical and communication Engineering, ICCECE 2025

作者： Kandaswamy, Vijay Anand Joshua Kumaresan, S. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331513894

This paper proposes a novel approach to video summarisation by introducing a feature variance model that enhances video frame extraction. In typical video record aggregation systems, a variety of image processing tasks, such as acquisition, preprocessing, segmentation, and feature extraction, should be carried out with high precision to guarantee seamless system performance. Among these, feature extraction is essential since it breaks down large amounts of visual data into more manageable, informative vectors, and the success of the system is directly impacted by how effectively this process functions. Widely Recurrent Neural Networks (RNNs) are used in supervised video summarisation processes to obtain a model for the temporal dependencies between video frames. RNN's ability to learn long-range memory in the video is limited, and CNN often focusses on local information while losing long-term knowledge when the input sequence is lengthy. Therefore, a Gated Recurrent Unit based Neural Network (GRU-NN) for video summarisation is proposed, to classify them into keyframes and non-keyframes. Keyframes are selected for summarisation, while non keyframes are discarded or reduced. We explore various keyframe extraction techniques, analyse the performance of the proposed summarisation model, and discuss the results. Finally, we offer insights into the model's potential improvements and its broader applications in video summarisation tasks. © 2025 IEEE.

关键词： Image segmentation

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Research on fine recognition method of electric power equipment safety hazards based on visual processing

Research on fine recognition method of electric power equipm...

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2024 International conference on Computer Vision and Image processing, CVIP 2024

作者： Huo, Yongbo Pang, Bingyao Yang, Xizai Yang, Le Lin, Yiming State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Xi’an710048 China School of Electrical Engineering Xi’an University of Technology Xi’an710048 China

ISBN: (纸本)9781510688384

With the further research of deep learning, power companies have gradually eliminated the prevention and control by manual inspection, and have adopted deep learning to identify the safety hazards of power equipment, and to detect the defects of power equipment more accurately and quickly. Firstly, this paper introduces the significant drawbacks of traditional power equipment identification methods in the field of power equipment identification, and then illuminates the advantages of deep learning in the field of power equipment, which leads to the YOLOv7 algorithm used in this study, and then provides a detailed introduction of the theory and concepts of the algorithm. Second, this paper constructs a dataset applicable to the identification of safety hazards in electrical equipment, and uses this dataset to train and test the YOLOv7 algorithm. Finally, by analyzing the experimental results, it is concluded that the algorithm has a high accuracy character and robustness in the method of identifying safety hazards of electrical equipment. This study provides a practical and feasible technical route and theoretical foundation for solving the problem of safety hidden hazard detection of electrical equipment using YOLOv7 algorithm. © 2025 SPIE.

关键词： Power transmission lines

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Fourth International conference on Computer Vision, Application, and Algorithm, CVAA 2024

Fourth International Conference on Computer Vision, Applicat...

引用

4th International conference on Computer Vision, Application, and Algorithm, CVAA 2024

ISBN: (纸本)9781510687615

The proceedings contain 122 papers. The topics discussed include: DFrFT-ES model for emotion recognition based on fractional Fourier transform of EEG signals;research on traffic sign recognition under complex meteorological conditions;diffusion-augmented learning for long-tail recognition;apple leaf scab recognition using CNN and transfer learning;container image management in cloud-edge environments: an image deletion method based on layer affinity;computer graphics and image processing techniques based on visual communication design;dynamic fusion and non-negative matrix factorization-based multi-view clustering method;convolutional recurrent neural network-based EEG signal classification in motor imagery;and sentiment classification of MOOC courses by merging local context focus and bi-directional gated recurrent unit.

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Corresponding an Audio-processing Transformer to EEG Brain Activity Induced by Naturalistic Audio-visual Video Stimuli

Corresponding an Audio-Processing Transformer to EEG Brain A...

引用

International conference on Artificial Intelligence in information and communication (ICAIIC)

作者： Hiroki Kurashige Jun Kaneko School of Information Telecommunication Engineering Research & Information Center (TRIC) Institute of Advanced Biosciences Tokai University Tokyo Japan School of Information Telecommunication Engineering Tokai University Tokyo Japan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

We attempted to relate EEG brain activities evoked by naturalistic audio-visual video stimuli to the outputs of an audio-processing Transformer induced by audio inputs extracted from the same stimuli. We found a good correspondence, especially in low-frequency brain activity. This is complementary to a previous study that showed good correspondence between high-frequency brain activity and a movie-processing Transformer. These suggest that combining audio- and movie-processing Transformers and using them as a brain simulator is promising. That is, by utilizing this, it should be possible to synthesize audio-visual video stimuli that can intervene in a variety of brain activities and functions.

关键词： Brain Artificial neural networks Transformers Electroencephalography Artificial intelligence

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An Optimized Tennis Ball Picking Method Combining YOLOv5 and K-Means++ 24

An Optimized Tennis Ball Picking Method Combining YOLOv5 and...

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8th International conference on Electronic information Technology and Computer Engineering, EITCE 2024

作者： Li, Peixiang Zhou, Haiyan Yang, Xingyu School of Information and Communication Guilin University of Electronic Technology Guangxi Guilin China

ISBN: (纸本)9798400710094

With the rapid development of artificial intelligence and robotics, intelligent picking robots are being increasingly applied across various fields. However, how to efficiently identify and pick up the target object in a complex dynamic environment is still a challenging problem. Aiming at this problem, an optimized visual recognition and path planning method is developed by fusing YOLOv5 target detection algorithm and K-Means++ clustering algorithm to improve the performance of tennis picking robot. YOLOv5 is employed within the ROS framework to detect and locate tennis balls, while K-Means++ is used to cluster and optimize their positions. Field experiments demonstrate that the system achieves mAP@0.5, mAP@0.5:0.95, accuracy, and recall rates of 98.26%, 86.14%, 99.14%, and 96.59%, respectively, in tennis ball recognition. The improved K-Means++ clustering algorithm shows strong performance in optimizing path planning. Compared to the unimproved K-Means algorithm, the average picking time is reduced by 12.14%. When compared to the Ant Colony Optimization, the reduction is 4.281%, and compared to the Genetic Algorithm, the picking time is decreased by 8.076%. Additionally, when compared to manual picking, the time is reduced by 19.589%. © 2024 Copyright held by the owner/author(s).

关键词： Genetic algorithms

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