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

作者： Weixiang Xu Zhongren Dong Runming Wang Xinzhou Xu Zixing Zhang College of Computer Science and Electronic Engineering Hunan University China School of Information Science and Engineering Hunan Normal University China Signal Processing and Speech Communication Laboratory Graz University of Technology Austria Shenzhen Research Institute Hunan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, multimodal emotion recognition (MER) has gained significant attention due to its potential to integrate information from diverse signals. However, existing methods often struggle to effectively capture complex interactions and contextual information both inter- and intra-modalities, and even to extract the salient representations from pre-trained models. To address these issues, we propose a novel model, gated Mixture of Multimodal Experts (MoME) and Mixtral of Experts (MixMoE) models, namely GateM 2 Former. The gate mechanism is used to select the most relevant representations from pre-trained models. The MoME and MixMoE expert modules respectively learn the individual characteristics of each modality and the intrinsic alignment and potential interactions between modalities. Besides, we design a hierarchical merge structure to better suit the long sequence scenario (i. e., speech in our case). To verify the effectiveness of the introduced model, we conducted extensive experiments on the IEMOCAP and MELD datasets. The results show that GateM 2 Former, with a universal multimodal structure, is able to achieve the best results on IEMOCAP and MELD compared with other latest approaches.

关键词： Adaptation models Emotion recognition Semantics Speech recognition Logic gates signal processing Transformers Feature extraction Robustness Speech processing

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MRLCD-A: Lag-aware alignment for multivariate time series forecasting in multiple scenarios

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Information processing and Management 2025年第5期62卷

作者： Sun, Dezhi Qin, Jiwei Zhang, Zihao Qin, Xizhong Zhang, Huiguo School of Computer Science and Technology Xinjiang University Xinjiang Uygur Autonomous Region Urumqi 830046 China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830046 China School of Mathematics and System Science Xinjiang University Xinjiang Uygur Autonomous Region Urumqi 830046 China

In multivariate time series forecasting tasks, the varying degrees of lag relationships among multivariate data significantly increase the complexity of accurate predictions. A model must effectively capture long-term dependencies and address intricate lag correlations to achieve reliable long-term forecasting. This paper proposes a novel Multivariate Rolling Lag Correlation Detection-Alignment (MRLCD-A) method to tackle these challenges. The method identifies rolling correlations, calculates lag distances in multivariate sequence inputs, and aligns the lagged variables accordingly. Multivariate Time Series (MTS) forecasting uses a Channel Dependency (CD) approach. Experiments on time series datasets across various scenarios, including electricity, weather, exchange rates, and atmospheric carbon concentrations, demonstrate that the proposed method outperforms state-of-the-art models in forecasting general multivariate time series and predicting long-term time series data in real-world environments. © 2025 Elsevier Ltd

关键词： Prediction models

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Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification

arXiv

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arXiv 2025年

作者： Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory on Blind Signal Processing Chengdu610041 China Queen’s University Belfast BelfastBT3 9DT United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks under multipath propagation. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modeling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method in terms of channel estimation accuracy and achieves performance close to the derived Cramér–Rao lower bound. © 2025, CC BY.

关键词： Tensors

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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An Online Updating Multi-Threshold Method for Mobility Relationship Mining 9

An Online Updating Multi-Threshold Method for Mobility Relat...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Zeng Chen Keren Wang Zhengguo Xu Jiaqi Yao National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781509038237;9781509038220

With the popularity of the mobile phones and location-based social networks, rich location data has become widely available nowadays, enabling study on friendship detection based on human mobility. However, in some circumstances, limited by data collection techniques, only discrete location（such as location IDs） can be fetched which leads to methods of detecting friendship based on distance metric is unrealistic. This paper aims to detect friendship among users based on their mobility data where locations are represented by location IDs. By considering each user’s preference for location and each location’s popularity, the difference between friends and strangers gets larger than using merely frequency of meeting events. However, we discover that measure values obtained may vary greatly among different users and propose to maintain a threshold for each user. In addition, an online updating algorithm is presented to update thresholds when a new user’s mobility data is available. Experiments conducted on MIT Reality Mining project dataset show that the proposed updating method is practical and the proposed multi-threshold model outperforms the state-of-theart methods proposed by Wang et al.

关键词： friendship detection multi-threshold mobility spatiotemporal online update

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Stable Extended U-Net for Noise-Robust Speaker Verification

Stable Extended U-Net for Noise-Robust Speaker Verification

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

作者： Zonghui Wang Zhihua Fang Liang He School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Intelligence Science and Technology Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. Researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model’s performance improvement. Research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through subpixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness.

关键词： Training Correlation Attention mechanisms Convolution Data models Stability analysis Robustness Noise measurement Speech processing signal to noise ratio

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Design of a Crooked-Wire Antenna by Differential Evolution and 3D Printing

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International Journal of Cognitive Informatics and Natural Intelligence 2021年第4期15卷 1-16页

作者： Zhao, Fei Xu, Qinghui Zeng, Sanyou Science and Technology on Blind Signal Processing Laboratory United States China University of Geosciences China

Antenna design often requires dealing with multiple constraints in the requirements, and the designs can be modeled as constrained optimization problems (COPs). However, the constraints are usually very strange, and then the feasible solutions are hard to find. At the same time, the robustness for antenna design is an important consideration as well. To solve the above issues, the combination of differential evolution algorithm (DE) and 3D-printing technique is presented to design a new crookedwire antenna. In the design process, DE is adopted to handle the constraints since DE is simple and efficient in finding feasible solutions. The objective of the modeled COP, which is the sum of variance of the gain, axial ratio, and VSWR over the frequency band, is used to enhance the robustness of the antenna and widen the frequency band without additional computational cost. The precision of fabricating the antenna is ensured by using 3D-printing. The design of the NASA LADEE satellite antenna is chosen as an example to verify the method of this paper. Experimental results show that the performance of the evolved antenna meets the design requirements. © 2021 IGI Global. All rights reserved.

关键词： Satellite antennas

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Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification

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IEEE Transactions on Vehicular technology 2025年

作者： Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory on Blind Signal Processing Chengdu610041 China Queen’s University Belfast BelfastBT3 9DT United Kingdom School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

关键词： Tensors

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HNet: A Loss Rate Robust ISAR Imaging Framework Based on Hypernetwork

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Li, Xiaoyong Liu, Lei Huang, Jiale Li, Lei Bai, Xueru Zhou, Feng Xidian University Hangzhou Institute of Technology Hangzhou311231 China Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi'an710071 China Xidian University National Key Lab of Radar Signal Processing Xi'an710071 China Xidian University School of Aerospace Science and Technology Xi'an710071 China

Deep learning has significantly advanced the field of sparse inverse synthetic aperture radar (ISAR) imaging. Nonetheless, current methodologies often face constraints due to their reliance on fixed loss rates and types, which can limit practical applicability and result in elevated computational demands both spatially and temporally, as well as diminished generalizability. In tackle to these challenges, this paper introduces a resilient ISAR imaging architecture grounded in hypernetwork technology capable of accommodating diverse loss rates. Initially, we devise a sparse signal reconstruction technique that functions irrespective of the loss rate or matrix dimensions. Subsequently, a methodology for producing arbitrary loss rates is outlined. Building upon this foundation, we present a hypernetwork framework that harnesses hypernetworks to derive defocus characteristics from range-Doppler (RD) imagery across varying loss rates, subsequently transforming these insights into ideal parameters for the sparse signal reconstruction process. By executing the iterative algorithm with these optimized parameters, we achieve loss rate robust ISAR imaging. Comprehensive evaluations conducted on point simulated data, electromagnetic simulated data and measured data confirm the superiority of our proposed approach in terms of reconstruction accuracy and generalization capabilities. This advancement facilitates reliable ISAR imaging across a spectrum of loss rates. 1965-2011 IEEE.

关键词： Radar imaging

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A Novel Construction of Two-Dimensional Z-Complementary Array Code Sets Based on Extended Boolean Functions

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IEEE Transactions on Vehicular technology 2025年

作者： Tian, Liying Song, Xiaoshi Li, Yubo Zhang, Haijun Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Two-dimensional (2-D) array sets with good 2-D correlation properties have received considerable attention in wireless communication systems. This paper focuses on 2-D Z-complementary array code sets (ZCACSs), which have ideal 2-D correlation properties within a rectangular zone around the origin. By developing a theory linking 2-D extended Boolean functions with 2-D arrays, we establish an efficient construction of 2-D ZCACSs. In contrast to previous work, the proposed construction is capable of producing 2-D ZCACSs with new flexible parameters. Constructions of three-dimensional (3-D) and higher-dimensional ZCACSs are further investigated. We remark that 2-D and higher-dimensional complete complementary codes (CCCs) can also be generated from the proposed constructions. © 1967-2012 IEEE.

关键词： Codes (symbols)

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