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

作者： Li, Jizhen Tu, Weiping Yang, Yuhong Xu, Xinmeng Zhang, Yiqun Ren, Yanzhen NERCMS School of Computer Science Hubei Luojia Laboratory Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9798350368741

Recently, the state space model (SSM) represented by Mamba has shown remarkable performance in long-term sequence modeling tasks, including speech enhancement. However, due to substantial differences in sub-band features, applying the same SSM to all sub-bands limits its inference capability. Additionally, when processing each time frame of the time-frequency representation, the SSM may forget certain high-frequency information of low energy, making the restoration of structure in the high-frequency bands challenging. For this reason, we propose Cross- and Sub-band Mamba (CSMamba). To assist the SSM in handling different sub-band features flexibly, we propose a band split block that splits the full-band into four sub-bands with different widths based on their information similarity. We then allocate independent weights to each subband, thereby reducing the inference burden on the SSM. Furthermore, to mitigate the forgetting of low-energy information in the high-frequency bands by the SSM, we introduce a spectrum restoration block that enhances the representation of the cross-band features from multiple perspectives. Experimental results on the DNS Challenge 2021 dataset demonstrate that CSMamba outperforms several state-of-the-art (SOTA) speech enhancement methods in three objective evaluation metrics with fewer parameters. © 2025 IEEE.

关键词： band split spectrum restoration speech enhancement state space model

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Optimal Illumination Distance Metrics for Person Re-identification 21st

Optimal Illumination Distance Metrics for Person Re-identif...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Wang, Chao Wang, Zhongyuan Hu, Ruimin Wang, Xiaochen Zhou, Wen National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China College of Information Engineering Nanjing University of Finance Economics Nanjing China

ISBN: (纸本)9789819601240

Person re-identification (Re-ID) has been widely used in public security and surveillance. Due to the influence of different shooting times and locations, can lead to lighting variations in the images captured by the camera, and the lighting variations seriously affect the Re-ID performance. Existing methods utilize images under different lighting conditions to transform to uniform lighting conditions to eliminate the effect of lighting on Re-ID. However, these methods ignore the fact that different illumination metric distances have different recognition performances. Therefore, we propose the optimal illumination distance metric, by analyzing different light distances for pedestrians in different light conditions, we find that there exists an optimal light condition in which the distance between image pairs is smaller than the distance in other light conditions. The distance under the optimal lighting condition is used in the retrieval process as the distance between its final sorted image pairs. To verify the effectiveness of our method, Market-1501-IA and Duke-MTMC-IA datasets are constructed, and a large number of experiments are conducted under different lighting conditions to verify the existence of the optimal lighting conditions and the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting

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Toward zero trust in 5G Industrial Internet collaboration systems

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Digital communications and networks 2025年第2期11卷 547-555页

作者： Zhang, Han Zhang, Ziyan Chen, Liquan School of Cyber Science and Engineering Southeast University Nanjing210096 China ZTE Corporation Nanjing210012 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518057 China Purple Mountain Laboratories for Network and Communication Security Nanjing211118 China

With the introduction of 5G, users and devices can access the industrial network from anywhere in the world. Therefore, traditional perimeter-based security technologies for industrial networks can no longer work well. To solve this problem, a new security model called Zero Trust(ZT) is desired, which believes in "never trust and always verify". Every time the asset in the industrial network is accessed, the subject is authenticated and its trustworthiness is assessed. In this way, the asset in industrial network can be well protected, whether the subject is in the internal network or the external network. However, in order to construct the zero trust model in the 5G Industrial Internet collaboration system, there are still many problems to be solved. In this paper, we first introduce the security issues in the 5G Industrial Internet collaboration system, and illustrate the zero trust architecture. Then, we analyze the gap between existing security techniques and the zero trust architecture. Finally, we discuss several potential security techniques that can be used to implement the zero trust model. The purpose of this paper is to point out the further direction for the realization of the Zero Trust Architecture (ZTA) in the 5G Industrial Internet collaboration system. © 2024 Chongqing University of Posts and Telecommunications

关键词： 5G mobile communication systems

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HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Quality Assessment Metric

HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Q...

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

作者： Zheng, Yuanming Yao, Jiaxuan Deng, Xiangyu Yang, Yuhong Liao, Ruiqi Tu, Weiping Lin, Cedar National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. Guangdong China

ISBN: (纸本)9798350368741

Spatial audio quality evaluation is essential for applications like virtual and augmented reality, where accurate sound reproduction enhances user immersion. While subjective listening tests are the gold standard, they are costly and time-consuming. To address this, we propose HAPG-SAQAM, an objective metric for assessing timbre quality, spatial quality, and overall quality of binaural audio, guided by human auditory perception. Our contributions include: (1) the Multi-scale Auditory Guided Feature Extraction (MAGFE) module, incorporating gammatone frequency cepstral coefficients for better alignment with human perception;(2) Perceptual Weighted Loss (PWL), optimizing the weighting of timbre quality (TQ) and spatial quality (SQ) loss based on subjective test data;and (3) data augmentation techniques to enhance robustness by amplifying perceptual distortions. Experimental results show HAPG-SAQAM improves correlation with subjective scores by 10%, with ablation studies confirming the contributions of its components to enhanced spatial and overall audio quality. © 2025 IEEE.

关键词： auditory perception spatial audio Speech and audio quality assessment subjective experiment timbre quality

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Exploring Spatial Hybrid User Interface for Visual Sensemaking

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IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Tong, Wai Li, Haobo Xia, Meng Kam-Kwai, Wong Pong, Ting-Chuen Qu, Huamin Yang, Yalong Wuhan University National Engineering Research Center for Multimedia Software (NERCMS) Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science China VIVECO Wuhan China

We built a spatial hybrid system that combines a personal computer (PC) and virtual reality (VR) for visual sensemaking, addressing limitations in both environments. Although VR offers immense potential for interactive data visualization (e.g., large display space and spatial navigation), it can also present challenges such as imprecise interactions and user fatigue. At the same time, a PC offers precise and familiar interactions but has limited display space and interaction modality. Therefore, we iteratively designed a spatial hybrid system (PC+VR) to complement these two environments by enabling seamless switching between PC and VR environments. To evaluate the system's effectiveness and user experience, we compared it to using a single computing environment (i.e., PC-only and VR-only). Our study results (N=18) showed that spatial PC+VR could combine the benefits of both devices to outperform user preference for VR-only without a negative impact on performance from device switching overhead. Finally, we discussed future design implications. © 1995-2012 IEEE.

关键词： Data visualization

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Predictive Generation for Digital Twin Channel:Long-Batch Time-Frequency Construction

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China communications 2025年第4期22卷 161-173页

作者： Che Chang Hu Jie Kang Honghui Rui Hua Lyu Xingzai Wang Bo School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE CorporationShenzhen 518055China

Wireless communication systems that incorporate digital twin(DT)alongside artificial intelligence(AI)are expected to transform 6G networks by providing advanced features for predictive modeling and decision *** key component is the creation of DT channels,which form the basis for upcoming ***,the existing work of channel predictive generation only considers time dimension,distribution-oriented or multi-step slidingwindow prediction schemes,which is not accurate and efficient for real-time DT communication ***,we propose the wireless channel generative adversarial network(WCGAN)to tackle the issue of generating authentic long-batch channels for DT *** generator based on convolutional neural networks(CNN)extracts features from both the time and frequency domains to better capture the *** loss function is designed to ensure that the generated channels consistently match the physical channels over an extended period while sharing the same probability ***,the accumulating error from the slicing window has been *** simulation demonstrates that an accurate and efficient DT channel can be generated by employing our proposed WCGAN in various scenarios.

关键词： channel generation channel prediction deep learning digital twin(DT) generative adversarial network(GAN)

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MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification

arXiv

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arXiv 2025年

作者： Li, Yapeng Luo, Yong Zhang, Lefei Wang, Zengmao Du, Bo National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China

Transformer has been extensively explored for hyperspectral image classification. However, transformer poses challenges in terms of speed and memory usage because of its quadratic computational complexity. Recently the Mamba model has emerged as a promising approach, which has strong long-distance modeling capabilities while maintaining a linear computational complexity. However, representing the hyperspectral image is challenging for the Mamba due to the requirement for an integrated spatial and spectral understanding. To remedy these drawbacks, we propose a novel hyperspectral image classification model based on a Mamba model, named MambaHSI, which can simultaneously model long-range interaction of the whole image and integrate spatial and spectral information in an adaptive manner. Specifically, we design a spatial Mamba block to model the long-range interaction of the whole image at the pixel-level. Then we propose a spectral Mamba block to split the spectral vector into multiple groups, mine the relations across different spectral groups, and extract spectral features. Finally, we propose a spatial-spectral fusion module to adaptively integrate spatial and spectral features of a hyperspectral image. To our best knowledge, this is the first image-level hyperspectral image classification model based on the Mamba. We conduct extensive experiments on four diverse hyperspectral image datasets. The results demonstrate the effectiveness and superiority of the proposed model for hyperspectral image classification. This reveals the great potential of Mamba to be the next-generation backbone for hyperspectral image models. Codes are available at https://***/li-yapeng/MambaHSI. Copyright © 2025, The Authors. All rights reserved.

关键词： Image classification

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Attention Weighting and Conditional Entropy-driven Quantization Loss for Neural Audio Codecs

Attention Weighting and Conditional Entropy-driven Quantizat...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huiyu Zhang Weiping Tu Yuhong Yang Xinmeng Xu Yanzhen Ren Rong Zhu NERCMS School of Computer Science Hubei Luojia Laboratory Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Cyber Science and Engineering Wuhan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing end-to-end neural codecs have made great progress in preserving audio quality. Despite their success, they still face challenges in achieving accurate and efficient quantization. Specifically, these codecs often overlook which features have a greater impact on perceptual audio quality during quantization, leading to a quantization error distribution that fails to reflect the actual importance of latent features. They are also sensitive to unusual data points (outliers) because they use Mean Squared Error (MSE) to measure quantization errors, which can increase quantization noise or spectral artifacts. To address these limitations, we propose AW-CEQCodec which integrates an Attention Weighting (AW) module and a Conditional Entropy-driven Quantization (CEQ) loss. The AW enhances key regions of latent features before quantization, enabling more accurate quantizing critical features and reducing their quantization errors. After quantization, it restores global details from dequantized features, improving overall reconstruction. Moreover, the CEQ minimizes the uncertainty between latent and quantized features, effectively reflecting the distortion introduced by the quantization module. Experimental results on the CodecSuperb-STL dataset demonstrate that our method consistently outperforms baseline approaches, achieving superior audio quality at bitrates as low as 0.5 kbps, confirming its effectiveness in minimizing distortion and preserving perceptual quality. The reconstruction audio samples can be find at https://***/first-page.

关键词： Quantization (signal) Codecs Acoustic distortion Accuracy Uncertainty Speech coding Real-time systems Noise measurement Speech processing Signal to noise ratio

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Improving Speech Enhancement by Cross- and Sub-band Processing with State Space Model

Improving Speech Enhancement by Cross- and Sub-band Processi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jizhen Li Weiping Tu Yuhong Yang Xinmeng Xu Yiqun Zhang Yanzhen Ren NERCMS School of Computer Science Hubei Luojia Laboratory Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Cyber Science and Engineering Wuhan University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, the state space model (SSM) represented by Mamba has shown remarkable performance in long-term sequence modeling tasks, including speech enhancement. However, due to substantial differences in sub-band features, applying the same SSM to all sub-bands limits its inference capability. Additionally, when processing each time frame of the time-frequency representation, the SSM may forget certain high-frequency information of low energy, making the restoration of structure in the high-frequency bands challenging. For this reason, we propose Cross- and Sub-band Mamba (CSMamba). To assist the SSM in handling different sub-band features flexibly, we propose a band split block that splits the full-band into four sub-bands with different widths based on their information similarity. We then allocate independent weights to each sub-band, thereby reducing the inference burden on the SSM. Furthermore, to mitigate the forgetting of low-energy information in the high-frequency bands by the SSM, we introduce a spectrum restoration block that enhances the representation of the cross-band features from multiple perspectives. Experimental results on the DNS Challenge 2021 dataset demonstrate that CSMamba outperforms several state-of-the-art (SOTA) speech enhancement methods in three objective evaluation metrics with fewer parameters.

关键词： Measurement Time-frequency analysis Speech enhancement Signal processing Acoustics

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Improving Speech Enhancement by Cross- and Sub-band Processing with State Space Model

arXiv

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arXiv 2025年

关键词： Speech enhancement

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