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

作者： Ke Chen Zhihua Huang Liang He Yonghong Yan School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Department of Electronic Engineering Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics CAS Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Code-Switching (CS) Text-To-Speech (TTS) models have gained attention due to the increasing prevalence of multilingual communication. However, existing models struggle to meet the growing demand for personalized CS TTS, particularly in Zero-Shot (ZS) scenarios where the model should generate speech for an unseen speaker from reference speech. In this paper, we propose ZCS-CDiff, a zero-shot code-switching TTS system with the Conformer-based diffusion model. We disentangle speech features and use the diffusion model to precisely model these disentangled attributes, resulting in high-quality ZS CS speech. Additionally, we introduce a Conformer-based WaveNet as the denoising network within the diffusion model to further improve the accuracy of modeling different attributes. We also designed a speaker-assist module to help the model better handle speaker information extracted from the reference speech, resulting in CS speech with higher speaker similarity. Experimental results and ablation studies demonstrate that the ZS CS speech generated by ZCS-CDiff has good speech naturalness, intelligibility, and speaker similarity while confirming the effectiveness of our design choices. Audio samples are available 1 .

关键词： Training Adaptation models Accuracy Speech coding Noise reduction Speech enhancement signal processing Diffusion models Probabilistic logic Text to speech

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Novel Off-Grid Estimation Method for Direction-of-Arrival Estimation Based on Deep Learning

Novel Off-Grid Estimation Method for Direction-of-Arrival Es...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Jun Zhang Yanjun Zhang Jun Tao Jiang Zhu Zhanye Chen Yan Huang North electronics research institude co. LTD Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China Xi'an Institute of Space Radio Technology Xi'an China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

Direction of arrival (DOA) estimation has always been an important and widely studied topic. In practical scenarios, achieving higher resolution and timely response speed is always a priority. Incorporating deep learning (DL) techniques into DOA estimation confers a multitude of advantages, such as efficient computation time. Nonetheless, similar to traditional algorithms, they encounter challenges with grid resolution limitations. This paper introduces a novel deep learning framework for accurately estimating DOA of off-grid signals through regression task, effectively addressing grid mismatch issues. Experimental results confirm the enhanced efficacy of the proposed method under various scenarios, surpassing traditional and other DL methods.

关键词： Deep learning Microwave technology Microwave integrated circuits Direction-of-arrival estimation Accuracy Millimeter wave technology Estimation

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Byzantine-Resilient Multi-Agent Distributed Exact Optimization with Less Data

arXiv

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arXiv 2023年

作者： Zhai, Yang Liu, Zhi-Wei Yue, Dong Hu, Songlin Xie, Xiangpeng The Institute of Advanced Technology for Carbon Neutrality Nanjing University of Posts and Telecommunications Nanjing210023 China The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China

This paper studies the distributed multi-agent resilient optimization problem under the f-total Byzantine attacks. Compared with the previous work on Byzantine-resilient multi-agent exact optimization problems, we do not require the communication topology to be fully connected. Under the redundancy of cost functions, we propose the distributed comparative gradient elimination resilient optimization algorithm based on the traditional assumptions on strongly convex global costs and Lipschitz continuous gradients. Under this algorithm, we successfully prove that if the number of in-neighbors of each normal agent is greater than some constant and the parameter f satisfies certain conditions, all normal agents' local estimations of the global variable will finally reach consensus and converge to the optimized solution. Finally, the numerical experiments successfully verify the correctness of the results. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection

arXiv

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arXiv 2023年

作者： Tan, Chuangchuang Liu, Huan Zhao, Yao Wei, Shikui Gu, Guanghua Liu, Ping Wei, Yunchao Institute of Information Science Beijing Jiaotong University China Beijing Key Laboratory of Advanced Information Science and Network Technology China School of Information Science and Engineering Yanshan University China Hebei Key Laboratory of Information Transmission and Signal Processing China Center for Frontier AI Research IHPC A*STAR Singapore

Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse. While the primary focus of deepfake detection has traditionally centered on the design of detection algorithms, an investigative inquiry into the generator architectures has remained conspicuously absent in recent years. This paper contributes to this lacuna by rethinking the architectures of CNN-based generator, thereby establishing a generalized representation of synthetic artifacts. Our findings illuminate that the up-sampling operator can, beyond frequency-based artifacts, produce generalized forgery artifacts. In particular, the local interdependence among image pixels caused by upsampling operators is significantly demonstrated in synthetic images generated by GAN or diffusion. Building upon this observation, we introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset, comprising samples generated by 28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance, showcasing a remarkable 11.6% improvement over existing methods. The code is available at https://***/chuangchuangtan/NPRDeepfakeDetection. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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A Spatio-Temporal Scale Based Affective Neural Network for Emotion Detection in Conversation

A Spatio-Temporal Scale Based Affective Neural Network for E...

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Chinese Automation Congress (CAC)

作者： Jiang Li Xiaoping Wang Shanglin Lei Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology (HUST) Wuhan China Institute of Artificial Intelligence Huazhong University of Science and Technology (HUST) Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Key Laboratory of Brain-inspired Intelligent Systems Hubei Province Wuhan China

Emotion Detection in Conversation (EDC) has been implemented in numerous domains and is under increasing attention. The EDC task is highly challenging due to the need to incorporate both the context of the utterance and the identity of the participant. Some existing EDC approaches employ redundant network structures and inadequately model contextual utterances, resulting in limited performance gains. In this work, we devise a Spatio-Temporal scale based Affective neural Network (STANet) in a simplistic and efficacious manner. STANet consists of two main components, i.e., spatial scale network and temporal scale network, which are applied to extract spatial scale and temporal scale information in the conversation, respectively. Prior to spatio-temporal scale modeling, we encode the identity information of participants to examine its impact on the performance of emotion detection. We conduct substantial comparative experiments and ablation studies on three conversational emotion datasets, and the results demonstrate that our STANet realizes superior performance without taking advantage of external knowledge.

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Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype

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Phenomics 2023年第5期3卷 469-484页

作者： Bailiang Zhao Yan Wang Menghan Hu Yue Wu Jiannan Liu Qingli Li Min Dai Wendell QSun Guangtao Zhai Shanghai Key Laboratory of Multidimensional Information Processing East China Normal UniversityShanghai 200241China School of Health Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Pathology The Second People’s Hospital of WuhuWuhu 241000AnhuiChina Ophthalmology Department Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineShanghai 610101China Department of Oral Maxillofacial Head Neck Oncology Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineShanghai 200011China Institute of Image Communication and Information Processing Shanghai Jiao Tong UniversityShanghai 200240China

Thyroid cancer,a common endocrine malignancy,is one of the leading death causes among endocrine *** diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating ***,we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma(PTC)diagnosis and characterize PTC *** alleviate any concerns pathologists may have about using the model,we conducted an analysis of the used bands that can be interpreted pathologically.A spectra acquisition system was first built to acquire spectra of pathological section images from 91 *** obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC *** data of the corresponding patients were collected for subsequent model interpretability *** experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal *** spectral preprocessing method was used to process the spectra,and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection *** PTC detection model using mean centering(MC)and multiple scattering correction(MSC)has optimal performance,and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test *** model interpretable analysis,the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak,and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC ***,the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC *** addition,the correlation analysis was performed between the selected wavelengths and the clinical data,and the results show:the ref

关键词： Interpretable pathologic model Pathological analysis Intraoperative detection Spectral phenotype

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Robust flocking for non-identical second-order nonlinear multi-agent systems

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Autonomous Intelligent Systems 2021年第1期1卷 81-90页

作者： Xiuxian Li Housheng Su Li Li Department of Control Science and Engineering College of Electronics and Information Engineeringand Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 200092China Institute for Advanced Study Tongji UniversityShanghai 200092China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of ChinaHuazhong University of Science and TechnologyLuoyu Road 1037Wuhan 430074China

This paper investigates the robust flocking problem for second-order nonlinear systems with a leader and external *** contrast with most of second-order systems in the literature,the intrinsic dynamics here are nonlinear and non-identical that depend not only on the velocity but also on the position,which is more ***,the interaction topology is undirected and *** that the leader’s velocity may be constant or time-varying,two distributed flocking control laws have been proposed for two cases to make the differences of the velocities between all followers and the leader approach to zero *** proposed distributed flocking control laws are both model-independent which results in the effectiveness of the controllers to cope with the different intrinsic dynamics of the followers and the leader under some assumptions on boundedness of several *** example is given to illustrate the validity of the theoretical results.

关键词： Flocking Multi-agent systems Second-order nonlinear systems Non-identical dynamics

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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A supervised learning detection method with pre-processing of sparsity-based adaptive line enhancer

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Chinese Journal of Acoustics 2021年第4期40卷 496-510页

作者： JIN Shenglong CHI Cheng LI Yu HUANG Haining Institute of Acoustics Chinese Academy of SciencesBeijing 100190 Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of SciencesBeijing 100190 University of Chinese Academy of Sciences Beijing 100049

For the weak line-spectrum target detection of uniiianned underwater vehicles in the complex environment,a supervised learning detection method witli pre-processing of sparsity-based adaptive line enhancer(ALE)is *** method incorporates al_(p)-nonn sparse penalty into the cost function of ALE,and it also promotes the sparse regularization model to the 0processing of sparsity-based ALE,the entropy features of target beam spectrum become obviously *** the small sample learning ability of support ve'etor machine(SVM),the method classifies the entropy characteristic curve of beam spectrum and determines if the target *** simulation result sliows that with the-20 dB input SNR,the SNR gain of l_(1/2)-nonn sparsity-based ALE is 11.5 dB higher than that of conventional *** effectiveness of the method is verified by using the unmanned underwater vehicle(UUV)experimental *** the influence of wideband strong interferences,the false alarm rate is 3.5%and the detection rate is 95.8%,which improved the detection probability of weak line-spectrum targets.

关键词： sparsity underwater spectrum

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Extended State Observer Based Fault-Tolerant Predictive Control for a Four-Mecanum Wheels Mobile Robot

Extended State Observer Based Fault-Tolerant Predictive Cont...

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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Binghao Yang Dongliang Wang Ziling Wen Wu Wei Wenji Li Zhun Fan Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong China School of Department of Electronic and Information Engineering Shantou University Guangdong China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou Guangdong China School of Automation Science and Engineering South China University of Technology Guangzhou Guangdong China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment.

关键词： Fault tolerance Trajectory tracking Fault tolerant systems Wheels Transportation Kinematics Predictive models Safety State estimation Predictive control

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