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

作者： Zhijian Jiang Haoming Li Nengheng Zheng The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China

Deep learning has become a popular approach for improving acoustic echo cancellation (AEC) in communication systems. However, existing systems mostly rely on traditional delay estimation methods, which often result in performance degradation due to inaccurate delay estimation. Furthermore, most deep learning-based methods use single-stage networks, the limited learning capability of which hinders the performance under harsh echo conditions. To address these challenges, this paper proposes a two-stage system with dual-path alignment. The two-stage strategy performs echo suppression on the magnitude spectrum in the first stage, followed by phase correction in the second one. In addition, the first stage processes two parallel features: the magnitude spectrum and its exponential compressed version, with which dual-path alignment is conducted to improve delay estimation. Experiment results demonstrate the effectiveness of the proposed system for echo suppression in challenging scenarios involving long delay, double-talk and nonlinear distortion.

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Low Resolution Face Image Recognition Based on Consistent Discriminant Correlation Analysis with Weight Correction 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Lin, Xiaoan Li, Meihua Pei, Jihong Zhao, Yang College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Consistent Discriminant Correlation Analysis (CDCA) is an effective algorithm for dual-view learning. However, the importance of information from different samples and features may vary. In CDCA, the sample features from both views are equally weighted during training, which may not fully utilize the information contained in certain sample features. This can negatively impact the decision-making and predictive capabilities of the model. Additionally, CDCA is only applicable to dual-view data and cannot effectively utilize multi-view data. To address these issues, this paper extends CDCA to multi-view scenarios and proposes Consistent Discriminant Correlation Analysis with Weight Correction (CDCA-WC). The feature weighting approach used in this paper involves calculating the average weight of each feature using the ReliefF algorithm. The sample weighting method employed is based on a weighted loss function, where a larger weight indicates a stronger classification ability for the corresponding feature or sample. During the training process, CDCA-WC is used to obtain the feature subspace mappings for the training samples from multiple views. The data’s feature and sample dimensions are then adjusted based on their respective weights, and the adjusted data is used for a second round of training. During testing, the similarity between samples is measured using a KNN classifier. Experimental results on three publicly available face recognition datasets demonstrate that CDCA-WC can effectively improve the accuracy of low-resolution face image recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Face recognition

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Regularized Loose Coupled Deep Non-negative Basis Matrix Factorization for Low-Resolution Face Image Recognition 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Li, Jianhua Wang, Jinxin Pei, Jihong Zhao, Yang College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Low-resolution face image recognition is a research focus in computer vision and pattern recognition. In this paper, the potential relationship between matched high-resolution face and low-resolution face images is considered to propose the regularized loose coupled deep non-negative basis matrix factorization (RLCDNBMF) architecture, which uses regularized deep non-negative basis matrix factorization for high-resolution images and couples its coefficient matrix with the coefficient matrix obtained from low-resolution non-negative factorization, so that the discriminative information of high-resolution face images can be fully utilized to guide the non-negative matrix factorization of low-resolution face images. Comparative experiments are conducted on three mainstream face databases, and the experimental results show that the method can improve the accuracy of low-resolution face recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Non-negative matrix factorization

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Coupled Non-negative Matrix Factorization with Deep Part-Based Feature Learning for Low-Resolution Image Recognition 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Wang, Jinxin Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

The existing low-resolution (LR) image recognition methods based on coupled relationship learning of high- and low-resolution images usually take similar feature extraction steps for both high- and low-resolution images. However, the high- and low-resolution features thus obtained may differ in terms of information content and information scale. This leads to a robust coupled relationship between the obtained high- and low-resolution images feature is difficult to be learned. Therefore, a coupled non-negative matrix factorization with a deep part-based feature learning method is proposed for the LR image recognition task. This method is divided into two main steps. Firstly, the depth non-negative matrix factorization method by graph convolution is used to obtain a robust depth underlying basis images of HR images for feature representation. Then the coupled relationship between the depth basis images feature of the HR images and the LR basis images feature is learned. Learning More Robust LR Basis images features using depth underlying features of HR images to assist LR images. Improve the expression of LR basis images of features. Experimental results show that the method can provide higher recognition performance. It outperforms existing image recognition methods in image recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Image recognition

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Uncertainty-Aware Modeling for Improving Prediction of Toxicity 23

Uncertainty-Aware Modeling for Improving Prediction of Toxic...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Zhou, Xinlei Wang, Xizhao Zhou, Xinpeng Tu, Siwu Ranwang School of Mathematical Sciences Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China College of Electronic Information Guangxi Minzu University 530000 China Jiangxi Drug Inspection Center Nanchang330001 China

ISBN: (纸本)9798331528041

Accurately predicting molecular toxicity is challenging in drug discovery and chemical safety assessment. Except for the representation of molecular structure, the inherent class imbalance and uncertainty often complicate the task. In this work, we present UnBGMT (Uncertainty-Aware Balanced Graph Multi-set Transformer), which integrates uncertainty modeling into the Graph Multiset Transformer (GMT) framework to improve the reliability and accuracy of molecular toxicity predictions. Our method employs Monte Carlo dropout (MC dropout) to evaluate uncertainty, which is then used to guide an adaptive loss function that adjusts weights in training. This approach guides the model to focus on the minority and samples with middle-level uncer-tainty, mitigating issues related to class imbalance and improving model stability. We conducted sufficient experiments on the Tox21 dataset, demonstrating the superiority of UnBGMT by comparing performance with baseline models and conducting ablation studies. Our results highlight the benefits of incorporating uncertainty in toxicity prediction, which provides a deeper insight into the im-pact of uncertainty on model performance. © 2024 IEEE.

关键词： Prediction models

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Optimal respiratory waveform selection based on range-multiple beams using a MIMO radar 13

Optimal respiratory waveform selection based on range-multip...

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13th International Conference on Signal processing Systems, ICSPS 2021

作者： Wu, Wanhua Yang, Zhaocheng Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

In this paper, we propose an optimal respiratory waveform selection algorithm based on range-multiple beams using a 77GHz frequency modulated continuous wave (FMCW) multiple-input-multiple-output (MIMO) radar. Generally speaking, the human chest is a multi-scattering target and the optimal monitoring position changes in a small range during breathing movement. According to this motion feature, we roughly locate the target in the range-angle candidate box based on range fast Fourier transform (FFT) and Capon direction of arrival (DOA) algorithm, respectively. Additionally, the fixed beamforming is utilized to algin the detected target site which can reduce the interference of clutter and enhance the signal-to-noise ratio (SNR). Then, the extended differential and cross-multiply (DACM) algorithm is further applied for phase unwrapping and the optimal respiratory waveform is extracted based on the features of respiratory periodicity. Ultimately, the respiratory rate is estimated by the frequency-time phase regression (FTPR) algorithm. Experiments with and without interference are conducted and the results show that the proposed algorithm can obtain accurate respiratory rate with mean square errors (MSE) 0.6862 breath2/min compared with the reference vital signs data. © COPYRIGHT SPIE.

关键词： Beamforming

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Reciprocal collaboration network for 3D skeleton-based human motion prediction 3

Reciprocal collaboration network for 3D skeleton-based human...

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3rd International Conference on Computer Science and Communication Technology, ICCSCT 2022

作者： He, Zhiquan Zhang, Lujun Cao, Wenming College of Information Engineering Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Multimedia Information Service Engineering Technology Research Center Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781510661240

Noticing that the human motion in both the positive and inverted time sequence are predictable, we design a reciprocal network which contains a forward and a backward prediction network to learn this information respectively, and then train the two networks in a collaborative and iterative way with the guidance of consistency constraint. Based on the reciprocal network, we establish an error compensation network to approximate the error of forward prediction network for making the forecast closer to the real pose. Extensive experiments show that our approach outperforms most recent methods in both short and long term motion predictions on Human 3.6M and CMU Mocap. © 2022 SPIE.

关键词： Error compensation

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A cycle-consistent reciprocal network for visual correspondence 3

A cycle-consistent reciprocal network for visual corresponde...

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3rd International Conference on Computer Science and Communication Technology, ICCSCT 2022

作者： He, Zhiquan Zheng, Donghong Cao, Wenming College of Information Engineering Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Multimedia Information Service Engineering Technology Research Center Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9781510661240

Visual correspondence refers to building dense correspondences between two or more images of the same category. Ideally, the predicted keypoints output by the model can be back to the source image's keypoints through the same type of network. However, in practical situations, the predicted keypoints usually do not perfectly map back to the source image keypoints. In order to strengthen the cycle-consistency of the model, we propose a cycle-consistent reciprocal network. The network uses joint loss functions to alternately train forward and inverse models, which makes the two models subject to cycle constraints and perform better with the help of each other. Experiment results demonstrate the performance of the model is improved on three popular benchmarks and set a new state-of-the-art on the benchmark of PF-WILLOW. © 2022 SPIE.

关键词： Benchmarking

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Keypoints and Action Units Jointly Drive Talking Head Generation for Video Conferencing

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Shi, Wuzhen Xue, Zibang Wen, Yang Shenzhen University Shenzhen Key Laboratory of Digital Creative Technology Guangdong Province Engineering Laboratory for Digital Creative Technology Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China

This paper introduces a high-quality talking head generation method that is jointly driven by keypoints and action units, aiming to strike a balance between low-bandwidth transmission and high-quality generation in video conference scenarios. Existing methods for talking head generation often face limitations: they either require an excessive amount of driving information or struggle with accuracy and quality when adapted to low-bandwidth conditions. To address this, we decompose the talking head generation task into two components: a driving task, focused on information-limited control, and an enhancement task, aimed at achieving high-quality, high-definition output. Our proposed method innovatively incorporates the joint driving of keypoints and action units, improving the accuracy of pose and expression generation while remaining suitable for low-bandwidth environments. Furthermore, we implement a multistep video quality enhancement process, targeting both the entire frame and key regions, while incorporating temporal consistency constraints. By leveraging attention mechanisms, we enhance the realism of the challenging-to-generate mouth regions and mitigate background jitter through background fusion. Finally, a prior-driven super-resolution network is employed to achieve high-quality display. Extensive experiments demonstrate that our method effectively supports low-resolution recording, low-bandwidth transmission, and high-definition display. © 1991-2012 IEEE.

关键词： Bandwidth

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Clustering Algorithm with Learnable Distance for Categorical Data with Nominal and Ordinal Attributes 5

Clustering Algorithm with Learnable Distance for Categorical...

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5th International Conference on Data Science and information Technology, DSIT 2022

作者： Jia, Hong Zhong, Weiwei College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

For clustering analysis on categorical data, the distance measurement between two objects often plays a very important role. However, most of the existing categorical distance metrics do not distinguish between nominal attributes and ordinal attributes. That is, these metrics do not explore the information contained in ordinal values, and ignore the order relationship among them. Therefore, this paper proposes a novel clustering algorithm, which uses a united framework to measure the distance between nominal attributes and ordinal attributes while distinguishing the different characteristics between them. The basic idea of the proposed method is that, the attribute value pairs with larger co-occurrence probability in the same cluster may have smaller distances. Therefore, the distances between different categories are dynamically evaluated based on the current cluster structure of the data samples. Subsequently, the distances and cluster relationship are alternately learned until convergence. Experimental results show that the proposed algorithm has better robustness and performance than the existing counterparts on different kinds of categorical data sets. © 2022 IEEE.

关键词： Clustering algorithms

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