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Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

Journal of Shanghai Jiaotong University (science) 2025年 1-9页

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Masking Radar signals with Two Masks Overlapping in Frequency Domain 2

Masking Radar Signals with Two Masks Overlapping in Frequenc...

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2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Ma, Rui Zhou, Shenghua Ma, Hui Qing, Jingshu Li, Jizhuan Li, Aihua Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Beijing Institute of Remote Sensing Equipment Science and Technology on Millimeter-Wave Laboratory Beijing China

ISBN: (纸本)9798331515669

Electronic attack (EA) equipment can interfere radar systems by emitting interference signals with the assistant of the electronic support measurement (ESM) equipment. In this case, masking signalss can be designed to deceive ESM equipment and prevent EA equipment from emitting interference. This paper proposes an active anti-interference signal design method for Multiple-input Multiple-output (MIMO) radar. Designed masking signals cover a radar signal in the time domain, but the frequency band of the masking signals in the frequency domain does not completely overlap with the radar signal, making it difficult for an enemy ESM equipment to determine whether the obtained signal is a radar signal or interference masking signals. To reduce the cross correlation sidelobes between radar signals and masking signals, the masking signals are designed with upper and lower frequency bands with an identical center frequency, and then the spectrum of the masking signals can be efficient shifted to achieve the spectrum overlap, therefore, low cross correlation sidelobes can be ensured. Simulation results indicate that the propsoed algorithm can reduce the cross-correlation sidelobe level to the level of -100 dB, and it is also shown that the proposed algorithm is robust to the radar transmission delay. © 2024 IEEE.

关键词： Radar interference

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Electrochemical protein biosensors for disease marker detection:progress and opportunities

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Microsystems & Nanoengineering 2024年第3期10卷 1-20页

作者： Lanpeng Guo Yunong Zhao Qing Huang Jing Huang Yanbing Tao Jianjun Chen Hua-Yao Li Huan Liu School of Integrated Circuits Wuhan National Laboratory for OptoelectronicsOptics Valley LaboratoryHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Integrated CircuitsAnhui UniversityHefei 230601China School of Optoelectronic Materials and Technology Jianghan UniversityWuhan 430056China Department of Otorhinolaryngology Union HospitalTongji Medical CollegeHuazhong University of Science and Technology1277 Jiefang AvenueWuhan 430022China Wenzhou Institute of Advanced Manufacturing Technology Huazhong University of Science and TechnologyWenzhou 325000China

The development of artificial intelligence-enabled medical health care has created both opportunities and challenges for next-generation biosensor *** are extensively used as biological macromolecular markers in disease diagnosis and the analysis of therapeutic *** protein biosensors have achieved desirable specificity by using the specific antibody–antigen binding principle in ***,the active centers of protein biomarkers are surrounded by a peptide matrix,which hinders charge transfer and results in insufficient sensor ***,electrode-modified materials and transducer devices have been designed to increase the sensitivity and improve the practical application prospects of electrochemical protein *** this review,we summarize recent reports of electrochemical biosensors for protein biomarker *** highlight the latest research on electrochemical protein biosensors for the detection of cancer,viral infectious diseases,inflammation,and other *** corresponding sensitive materials,transducer structures,and detection principles associated with such biosensors are also addressed ***,we present an outlook on the use of electrochemical protein biosensors for disease marker detection for the next few years.

关键词： diagnosis specificity Electrochemical

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A S-transform Based Algorithm for Doppler Frequency Rate-of-Change Estimation from Coherent Pulse Train 3

A S-transform Based Algorithm for Doppler Frequency Rate-of-...

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2021 3rd International Conference on Computer, Communications and Mechatronics Engineering, CCME 2021

作者： Lan, Zhangfu Peng, Huafeng Zhao, Peiyan National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041 China

The traditional Fourier correlation method has the problem of poor accumulation effect, which leads to the low estimation accuracy of Doppler frequency change rate coherent pulse signal under the condition of low signal-to-noise ratio or short pulse duration. To solve this problem, an estimation algorithm based on S-transform is proposed to estimate the Doppler frequency change rate. By means of higher time-frequency aggregation of S transform, the coherent accumulation of the coherent pulse signal is better realized;Then the high accuracy Doppler frequency change rate is obtained by using the minimum variance criterion in the phase domain. Theoretical derivation and simulation results show that the algorithm in this paper can improve the accuracy of about 1 times compared with the traditional algorithm. It lays a solid foundation for the estimation of the high-precision Doppler frequency change rate in the practical application of subsequent projects, and improves the accuracy of target positioning and tracking. © Published under licence by IOP Publishing Ltd.

关键词： Frequency estimation

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Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segmentation Network Based on Local Attention and Mamba 27th

Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segment...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Feifei Shi, Fei Ren, Dayong Jia, Zhenhong Wang, Jianyi School of Computer Science and Technology Xinjiang University Urumqi Xinjiang830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang830046 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

ISBN: (纸本)9783031781032

Brain tissue segmentation is critical for diagnosing and treating brain diseases. While Mamba-based models excel in the medical field, they face performance bottlenecks with high-resolution MRI images, often losing local feature information in complex texture structures. To address these challenges and enable deployment in resource-limited settings, we propose Dual-MambaNet, a lightweight segmentation model based on Mamba. In Dual-MambaNet, we introduce the Outlook attention module to capture local complex textures and structures in brain MRI images. Subsequently, we combined it with the Mamba block to construct a feature extractor (FE) encoder layer to couple local and global features. Additionally, we integrate dual decoder branches and a multi-level pixel contrastive loss function(MPCL) to better integrate local and global features. This method optimizes global feature representation by refining local complex textures and structural details, effectively capturing multi-level features in MRI images. Experimental results on public brain MRI datasets OASIS1 and MRBrainS13 demonstrate that Dual-MambaNet achieves high segmentation accuracy with minimal parameters and computational complexity, making it suitable for deployment in resource-limited medical environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic resonance imaging

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Research on Spatial Angle Super-Resolution Algorithm Based on Adaptive Layered Sparse

Research on Spatial Angle Super-Resolution Algorithm Based o...

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2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Xue, Min Gao, Yuexin Wu, Zhixin Xidian University National Key Laboratory of Radar Signal Processing Xi'an China School of Aerospace Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798350318012

Compressed Sensing (CS) theory is based on the sparsity of signals and involves compressively sampling high-dimensional data to obtain a small number of linear observations that contain the complete information of the signals. By solving an optimization problem, the original signals can be recovered from these compressed linear observations. This paper combines CS theory with the sparse characteristics of targets in the spatial domain and proposes an adaptive layered sparse spatial angle super-resolution algorithm. This algorithm converts the target spatial angle super-resolution into the problem of using orthogonal basis to reconstruct sparse signals. Then, the target spatial angle super-resolution is carried out by using single snapshot data through optimization solution, and the target spatial angle domain is continuously reduced through layered orthogonal matching pursuit (LOMP) solution to complete the target spatial angle reconstruction. The effectiveness and robustness of the algorithm are verified by using the proposed algorithm to process the measured data. © 2023 IEEE.

关键词： Compressed sensing

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APPLICATION OF COMPLEX DEEP NEURAL NETWORKS IN COMPLEX DATA processing AND COMPARATIVE ANALYSIS OF ACTIVATION FUNCTIONS

APPLICATION OF COMPLEX DEEP NEURAL NETWORKS IN COMPLEX DATA ...

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IET International Radar Conference 2023, IRC 2023

作者： Yang, Junxiang Zhou, Ying Ma, Hui Lv, Kun Liu, Hongwei Liu, Lin National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Beijing Institute of Remote-sensing Equipment Science and Technology Millimeter-wave Laboratory Beijing China

ISBN: (纸本)9781839539954

The application of deep learning to fast radar data processing has important significance. The effectiveness of complex deep neural networks (CDNNs) has recently been demonstrated, and complex networks are important for processing complex data because they have the ability to handle phases. And the complex-valued activation function introduces a nonlinear transformation to the network, which has a great potential to improve the network performance. In this paper, we test the feasibility of CDNNs and the effect of different activation functions in the framework of complex networks under the goal of retaining more phase information. In particular, we propose a new complex-valued activation R ReLU based on ReLu and test its performance in a classification and auto-encoder context. Finally, we perform radar imaging reconstruction by different activation functions in complex networks, and the results show that R ReLU is more suitable for processing radar data in CDNNs. © The Institution of Engineering & technology 2023.

关键词： Deep neural networks

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A knowledge-assisted space-time spectrum compensation method for bistatic array radar clutter

A knowledge-assisted space-time spectrum compensation method...

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IET International Radar Conference 2023, IRC 2023

作者： Yu, Chunyu Tian, Min Liao, Guisheng Li, Yu Hangzhou Institute of Technology Xidian University Hangzhou China National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Xi'an Institute of Space Radio Technology National Key Laboratory of Science and Technology on Space Microwave Xi'an China

ISBN: (纸本)9781839539954

When the spaceborne bistatic radar looks down, radar returns from ground, referred to as clutter, may be powerful and cause severe interferences. Space-time adaptive processing is an effective way to suppress the clutter for moving target detection. However, as the space-time spectrum of clutter in the bistatic radar system is rapidly changing in space, the space-time spectrum in a range gate may be severely spread, resulting in deterioration of processing performance. In order to solve this problem, a knowledge-assisted space-time spectrum compensation method for the bistatic radar system is proposed. Specifically, the detection area is divided into small-scale resolution units, and the space-time clutter model of the bistatic system is established according to the system geometry, platform relative motion and radar detection mechanism, which forms prior knowledge of the space-time vectors. Then, the knowledge-assisted space-time matrixes of the clutter units in the range gate to be compensated is constructed. Meanwhile, the space-time matrixes of the reference clutter units is searched by the Capon spectrum in the circular domain to fit the original clutter data. Based on the two matrixes, a transformation matrix is constructed to realize the space-time spectrum compensation. Finally, the validation of the proposed compensation algorithm is present by simulations. © The Institution of Engineering & technology 2023.

关键词： Radar clutter

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Clutter Compensation for Space-Air Bistatic Radar Based on Unitary Subspace Transformation

Clutter Compensation for Space-Air Bistatic Radar Based on U...

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2023 IEEE International Radar Conference, RADAR 2023

作者： Kan, Qingyun Xu, Jingwei Liao, Guisheng Wang, keyi Xu, Yanhong Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Xi'an University of Science and Technology The School of Communication and Information Engineering Xi'an China

ISBN: (纸本)9781665482783

Since the space-air bistatic radar (SABR) system works in down-looking mode, it encounters a severely broadened clutter spectrum and the clutter is range dependent. Thereby diminishing the performance of space-time adaptive processing (STAP) technology through covariance matrix estimation error. It is necessary to perform clutter compensation before STAP clutter suppression. Inspired by the thinking of coherent signal subspace method for estimating the directions of arrival of wideband signals, we propose the range dependence compensation method which transforms the clutter subspace of training samples into the clutter subspace of reference range bin by unitary matrix transformation. Simulation examples are conducted to validate the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Space time adaptive processing

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Cooperative Optimization Method of UAV Swarm for Multi-objective High-Precision Location 5th

Cooperative Optimization Method of UAV Swarm for Multi-objec...

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Li, Bowen Ji, Xiaoting Huang, Yuanling School of Information System Engineering Information Engineering University Zhengzhou450001 China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041 China

ISBN: (纸本)9789811939976

In the field of electromagnetic radiation source location, using UAVs to locate the radiation source target has become a new location method. At present, the UAV swarm cooperative optimization method for high-precision location has some challenges, such as insufficient ability of multi-objective high-precision location, poor timeliness and so on. To solve these problems, this paper proposes a cooperative optimization method of UAV swarm for multi-objective location. Firstly, the multi-objective joint location accuracy is derived, which is taken as the objective function of swarm cooperative optimization. Secondly, in order to allocate multiple UAVs to multiple targets efficiently, the initialization strategy based on target isolation and the random allocation method of UAV based on probability are proposed. Finally, by combining the above improved strategies, an optimization method of UAV swarm cooperative location is proposed. Through the analysis of experimental results, the cooperative optimization method in this paper has obvious advantages in global optimization and convergence ability, which can improve the joint location accuracy and optimization speed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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