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2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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The Method of processing Abnormal Values for Flight Test Data Based on Wavelet Transform

The Method of Processing Abnormal Values for Flight Test Dat...

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Electronic Information Engineering and Computer Technology (EIECT), International Conference on

作者： Jingxiang Xu Lei Zuo Zheng Zhao National Laboratory of Radar Signal Processing Xidian University Xi’an China State Key Laboratory of Experimental Physics and Computational Mathematics Beijing China

In the flight test data, there usually are abnormal values due to uncertainties in acquisition, storage and transmission. processing the abnormal values is essential to ensure the accuracy of subsequent test data analysis. In this paper, we propose an abnormal value processing method based on wavelet transform for flight test data. We use the summation of high- frequency wavelet details as the characteristic quantity for abnormal value detection. Moreover, the detection threshold is constructed based on the idea of constant false alarm detection to locate and process the abnormal values. Simulation results show that the method can detect and process the abnormal values in the time-series flight test data fast and accurately.

关键词： Wavelet transforms Uncertainty Data analysis Simulation Feature extraction Robustness High frequency

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DDNet: Deformable Convolution and Dense FPN for Surface Defect Detection in Recycled Books

DDNet: Deformable Convolution and Dense FPN for Surface Defe...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jun Yu WenJian Wang School of Computer and Information Technology (School of Big Data) Shanxi University Taiyuan China Taihang Laboratory In Shanxi Province (Advanced Computing Laboratory In Shanxi Province) Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recycled and recirculated books, such as ancient texts and reused textbooks, hold significant value in the secondhand goods market, with their worth largely dependent on surface preservation. However, accurately assessing surface defects is challenging due to the wide variations in shape, size, and the often imprecise detection of defects. To address these issues, we propose DDNet, an innovative detection model designed to enhance defect localization and classification. DDNet introduces a surface defect feature extraction module based on a deformable convolution operator (DC) and a densely connected FPN module (DFPN). The DC module dynamically adjusts the convolution grid to better align with object contours, capturing subtle shape variations and improving boundary delineation and prediction accuracy. Meanwhile, DFPN leverages dense skip connections to enhance feature fusion, constructing a hierarchical structure that generates multi-resolution, high-fidelity feature maps, thus effectively detecting defects of various sizes. In addition to the model, we present a comprehensive dataset specifically curated for surface defect detection in recycled and recirculated books. This dataset encompasses a diverse range of defect types, shapes, and sizes, making it ideal for evaluating the robustness and effectiveness of defect detection models. Through extensive evaluations, DDNet achieves precise localization and classification of surface defects, recording a mAP value of 46.7% on our proprietary dataset—an improvement of 14.2% over the baseline model—demonstrating its superior detection capabilities.

关键词： Location awareness Accuracy Convolution Shape Semisupervised learning Feature extraction Robustness Speech processing Surface treatment Defect detection

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MEFFGRN: Matrix enhancement and feature fusion-based method for reconstructing the gene regulatory network of epithelioma papulosum cyprini cells by spring viremia of carp virus infection

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Computers in Biology and Medicine 2024年 179卷 108835-108835页

作者： Wei, Pi-Jing Bao, Jin-Jin Gao, Zhen Tan, Jing-Yun Cao, Rui-Fen Su, Yansen Zheng, Chun-Hou Deng, Li Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University 111 Jiulong Road HefeiAnhui230601 China Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University 111 Jiulong Road HefeiAnhui230601 China Shenzhen Key Laboratory of Microbial Genetic Engineering College of Life Sciences and Oceanology Shenzhen University Guangdong Shenzhen518055 China School of Artificial Intelligence Anhui University 111 Jiulong Road HefeiAnhui230601 China

Gene regulatory networks (GRNs) are crucial for understanding organismal molecular mechanisms and processes. Construction of GRN in the epithelioma papulosum cyprini (EPC) cells of cyprinid fish by spring viremia of carp virus (SVCV) infection helps understand the immune regulatory mechanisms that enhance the survival capabilities of cyprinid fish. Although many computational methods have been used to infer GRNs, specialized approaches for predicting the GRN of EPC cells following SVCV infection are lacking. In addition, most existing methods focus primarily on gene expression features, neglecting the valuable network structural information in known GRNs. In this study, we propose a novel supervised deep neural network, named MEFFGRN (Matrix Enhancement- and Feature Fusion-based method for Gene Regulatory Network inference), to accurately predict the GRN of EPC cells following SVCV infection. MEFFGRN considers both gene expression data and network structure information of known GRN and introduces a matrix enhancement method to address the sparsity issue of known GRN, extracting richer network structure information. To optimize the benefits of CNN (Convolutional Neural Network) in image processing, gene expression and enhanced GRN data were transformed into histogram images for each gene pair respectively. Subsequently, these histograms were separately fed into CNNs for training to obtain the corresponding gene expression and network structural features. Furthermore, a feature fusion mechanism was introduced to comprehensively integrate the gene expression and network structural features. This integration considers the specificity of each feature and their interactive information, resulting in a more comprehensive and precise feature representation during the fusion process. Experimental results from both real-world and benchmark datasets demonstrate that MEFFGRN achieves competitive performance compared with state-of-the-art computational methods. Furthermore, study

关键词： Gene expression

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Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens

arXiv

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arXiv 2024年

作者： Sun, Dengdi Pan, Yajie Lu, Andong Li, Chenglong Luo, Bin The Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China The Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

Many RGBT tracking researches primarily focus on modal fusion design, while overlooking the effective handling of target appearance changes. While some approaches have introduced historical frames or fuse and replace initial templates to incorporate temporal information, they have the risk of disrupting the original target appearance and accumulating errors over time. To alleviate these limitations, we propose a novel Transformer RGBT tracking approach, which mixes spatio-temporal multimodal tokens from the static multimodal templates and multimodal search regions in Transformer to handle target appearance changes, for robust RGBT tracking. We introduce independent dynamic template tokens to interact with the search region, embedding temporal information to address appearance changes, while also retaining the involvement of the initial static template tokens in the joint feature extraction process to ensure the preservation of the original reliable target appearance information that prevent deviations from the target appearance caused by traditional temporal updates. We also use attention mechanisms to enhance the target features of multimodal template tokens by incorporating supplementary modal cues, and make the multimodal search region tokens interact with multimodal dynamic template tokens via attention mechanisms, which facilitates the conveyance of multimodal-enhanced target change information. Our module is inserted into the transformer backbone network and inherits joint feature extraction, search-template matching, and cross-modal interaction. Extensive experiments on three RGBT benchmark datasets show that the proposed approach maintains competitive performance compared to other state-of-the-art tracking algorithms while running at 39.1 FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection

arXiv

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arXiv 2022年

作者： Liu, Zhengyi Tan, Yacheng He, Qian Xiao, Yun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China

Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model, SwinNet, for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity ***://***/liuzywen/SwinNet © 2022, CC0.

关键词： Convolutional neural networks

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A robust incomplete large-scale group decision-making model for metaverse metro operations and maintenance

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Applied Soft Computing 2024年 156卷

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

The metaverse, constructed through digital technology, serves as a virtual realm intertwining with reality. Within this context, the challenge of evaluating data from diverse sources arises, and the application of large-scale group decision-making (LSGDM) methods emerges as a viable solution. Handling incomplete information and reducing dimensionality for large-scale decision-makers (DMs) is crucial in addressing complex decision-making problems. Moreover, addressing missing data is a fundamental and pivotal concern in tackling real-world decision challenges, given the ubiquitous presence of information gaps that cannot be straightforwardly integrated into decision models. Besides, the intricacies of LSGDM amplify this challenge by introducing a wealth of DMs, thereby augmenting the complexity and diversity of decision-related information. This paper proposes an approach to supplement missing data by double-dimensions. This paper explores various facets of similarity relationships within the data to enhance data completeness. Additionally, this paper categorizes DMs into clusters based on their relevance and establishes a two-stage consensus-reaching process (CRP) that takes into account both group sizes and individual consensus contributions. These CRPs play a crucial role in enhancing the overall consistency and consensus within the decision group. Subsequently, this paper applies a robust decision-making method rooted in MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the complete MULTIplicative form) to rank decision objects. Finally, this paper employs this proposed methodology in a practical case study that involves evaluating the operational status of a metaverse's urban construction metro system. Following these considerations, a comprehensive stability analysis of relevant parameters is conducted to guarantee the robustness and reliability of the decision-making process. © 2024 Elsevier B.V.

关键词： Multiobjective optimization

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Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

arXiv

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arXiv 2024年

作者： He, Fan He, Mingzhen Shi, Lei Huang, Xiaolin Suykens, Johan A.K. STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai200433 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Ridgeless regression has garnered attention among researchers, particularly in light of the "Benign Overfitting" phenomenon, where models interpolating noisy samples demonstrate robust generalization. However, kernel ridgeless regression does not always perform well due to the lack of flexibility. This paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths (LAB) RBF kernels, incorporating kernel learning techniques to improve performance in both experiments and theory. For the first time, we demonstrate that functions learned from LAB RBF kernels belong to an integral space of Reproducible Kernel Hilbert Spaces (RKHSs). Despite the absence of explicit regularization in the proposed model, its optimization is equivalent to solving an 0-regularized problem in the integral space of RKHSs, elucidating the origin of its generalization ability. Taking an approximation analysis viewpoint, we introduce an lq-norm analysis technique (with 0 © 2024, CC BY.

关键词： Vector spaces

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A decentralized Nesterov gradient method for stochastic optimization over unbalanced directed networks

A decentralized Nesterov gradient method for stochastic opti...

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作者： Hu, Jinhui Xia, Dawen Cheng, Huqiang Feng, Liping Ji, Lianghao Guo, Jing Li, Huaqing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China College of Data Science and Information Engineering Guizhou Minzu University Guiyang China Department of Computer Science Xinzhou Teachers University Shanxi China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

Decentralized stochastic gradient methods play significant roles in large-scale optimization that finds many practical applications in machine learning and coordinated control. This paper studies optimization problems over unbalanced directed networks, where the mutual goal of agents in the network is to optimize a global objective function expressed as a sum of local objective functions. Each agent using only local computation and communication in the networks is assumed to get access to a stochastic first-order oracle. In order to devise a noise-tolerant decentralized algorithm with accelerated linear convergence, a decentralized Nesterov gradient algorithm with the constant step-size and parameter using stochastic gradients is proposed in this paper. The proposed algorithm employing a gradient-tracking technique is proved to converge linearly to an error ball around the optimal solution via the analysis on a linear system when the positive constant step-size and parameter are sufficiently small. We further recover the exact linear convergence for the proposed algorithm with exact gradients under the same selection conditions of the constant step-size and parameter. Some real-world data sets are used in simulations to validate the correctness of the theoretical findings and practicability of the proposed algorithm. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.

关键词： Multi agent systems

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Tsi-Gcn: Translation and Scaling Invariant Gcn for 3d Point Cloud Analysis

SSRN

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SSRN 2024年

作者： Du, Zijin Liang, Jiye Yao, Kaixuan Cao, Feilong Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Department of Applied Mathematics China Jiliang University Zhejiang Hangzhou310018 China

Point cloud is a crucial data format for 3D vision, but its irregularity makes it challenging to comprehend the associated geometric information. Although some previous research has attempted to improve deep learning on point cloud and achieved promising results, they often overlook the robust shape descriptors of 3D targets, making them susceptible to translation and scaling transformations. This paper proposes a novel framework for point cloud analysis, to achieve feature extraction with translation and scaling invariance. It mainly includes local adaptive kernel, translation and scaling invariant convolution (TSIConv), and graph attention pooling. The key component is the design of TSIConv, which extracts the shape information with translation and scaling invariance. Then it performs convolution with local adaptive kernels to capture the features in various shape structures. Following the convolution layer, we add the graph attention pooling to coarsen point cloud, thus achieving multi-scale analysis and computational overhead reduction. The proposed framework, consisting of two networks, completes point cloud classification and part segmentation tasks in an end-to-end manner. The property analysis and experiments demonstrate that our model strictly guarantees the translation and scaling invariance, meanwhile achieving comparable performance to previous methods. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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