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

作者： Hejing Zhang Qiaoxi Zhu Jian Guan Haohe Liu Feiyang Xiao Jiantong Tian Xinhao Mei Xubo Liu Wenwu Wang Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University Harbin China National Engineering Laboratory for Modeling and Emulation in E-Government Harbin Engineering University Harbin China Centre for Audio Acoustics and Vibration University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford UK

First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types are unseen in training. Existing methods often rely on the availability of normal and abnormal sound data from the target machines. However, due to the lack of anomalous sound data for the target machine types, it becomes challenging when adapting the existing ASD methods to the first-shot task. In this paper, we propose a new framework for the first-shot unsupervised ASD, where metadata-assisted audio generation is used to estimate unknown anomalies, by utilising the available machine information (i.e., metadata and sound data) to fine-tune a text-to-audio generation model for generating the anomalous sounds that contain unique acoustic characteristics accounting for each different machine type. We then use the method of Time-Weighted Frequency domain audio Representation with Gaussian Mixture Model (TWFRGMM) as the backbone to achieve the first-shot unsupervised ASD. Our proposed FS-TWFR-GMM method achieves competitive performance amongst top systems in DCASE 2023 Challenge Task 2, while requiring only 1% model parameters for detection, as validated in our experiments.

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Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection

Learning on Gradients: Generalized Artifacts Representation ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chuangchuang Tan Yao Zhao Shikui Wei Guanghua Gu Yunchao Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology School of Information Science and Engineering Yanshan University Hebei Key Laboratory of Information Transmission and Signal Processing

Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of 11.4%. The code is released at https://***/chuangchuangtan/LGrad.

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A Speaker Recognition Method Based on Stable Learning

A Speaker Recognition Method Based on Stable Learning

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

作者： Jian Zhang Jing Ma Xiaochen Guo Lin Li Liang He School of Computer Science and Technology Xinjiang University China Xinjiang Key Laboratory of Signal Detection and Processing China School of Electronic Science and Engineering Xiamen University China Department of Electronic Engineering Tsinghua University China

With the development of deep learning, speaker recognition systems have shown increasingly better performance. The generalization ability of the models is also an important aspect of performance evaluation. Typically, a baseline system is used to compare against the improved models to demonstrate performance enhancements. However, we cannot determine the differences in learned voiceprint features between the improved models and the baseline system. This paper introduces an improved speaker recognition system based on the ECAPA-TDNN model. It utilizes stable learning to eliminate sample correlation and employs attribution analysis to compare the differences in voiceprint feature learning between the improved and baseline systems. Experimental results demonstrate that stable learning improves the model’s generalization performance and helps it learn better voiceprint features. The effectiveness and generalization capability of the proposed method are verified through experiments on the VoxCeleb, CNCeleb, and LibriSpeech datasets. This work is important for enhancing speaker recognition performance, analyzing differences in voiceprint feature learning, and promoting advancements in the field.

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Analysis of sea surface velocity vector inversion error based on multi-static ATI-SAR

Analysis of sea surface velocity vector inversion error base...

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IET International radar Conference (IRC 2023)

作者： Huancheng Guo Zhibin Wang JiaHan Lou Lei Liu Fanyi Tang Xuemao Li Qi Zhang Zhenfang Li Qingjun Zhang National Key Laboratory of Radar Signal Processing Xidian University Xi’an People's Republic of China Country Institute of Remote Sensing Satellite China Academy of Space Technology Beijing People's Republic of China

ISBN: (数字)9781837240982

Multi-static ATI SAR has significant potential for measuring sea surface velocity vectors. The system comprises one signal-transmitting satellite and two ATI signal-receiving satellites, with a substantial baseline between the receiving satellites and the transmitting satellite. This paper analyzes the accuracy of measuring sea surface velocity vectors using a multi-static spaceborne ATI SAR system, encompassing both velocity and direction measurements. To begin, we revisit the principles of sea surface velocity measurement based on the interferometric SAR's observation geometry to identify potential sources of error in the inversion of the sea surface current field. Subsequently, we derive formulas to quantify the impact of two types of errors: random errors and systematic errors, on the accuracy of vector inversion. Finally, we elucidate the satellite system requirements for multi-static ATI systems by utilizing a set of system parameters.

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A Symbol Rate Estimation Scheme for Non-Cooperative Communication 7

A Symbol Rate Estimation Scheme for Non-Cooperative Communic...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Lu, Jian Xu, Wenbo Wang, Siye Qi, Huiying Li, Yabin Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Beijing China National Key Laboratory of Science and Technology On Blind Signal Processing Shanghai China

ISBN: (纸本)9781665409506

In non-cooperative communication, symbol rate estimation is a prerequisite for signal demodulation. However, it is particularly difficult to estimate the symbol rate in the scenario with unknown modulation type and root cosine roll-off shaping. In this paper, an estimator without the requirement of communication parameters is proposed. First, the signal is recognized as constant envelope or non-constant envelope signal by square spectrum. Then, for the former, the symbol rate estimation is based on wavelet transform;for the latter, an envelope velocity strategy is adopted. In the proposed scheme, we elaborate some design details, including resampling factor, extraction of discrete spectral lines in the squared spectrum, selection of wavelet transform scale, and compensation of frequency deviation. We further propose a low-complexity implementation to meet the requirements for real-time capability and accuracy. Simulation results show that our proposed scheme is effective and accurate. © 2021 IEEE.

关键词： Wavelet transforms

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Multi-Layer Visual Perception for No-Reference Image Quality Assessment 6

Multi-Layer Visual Perception for No-Reference Image Quality...

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6th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2023

作者： Qi, Junwei Wang, Yingzhen Wang, Qingchun Li, Yingsong College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei230601 China

ISBN: (纸本)9798350399059

In this work, we propose a no-reference image quality evaluation approach, aiming to solve the problem that the traditional convolutional neural network is insufficient to express the global information of the image. Therefore, in order to make the captured image features have the coherence of global context information, we combine a Transformer structure widely used in NLP (natural language processing) with the traditional CNN to realize the attention mechanism in the human visual perception characteristics. Firstly, the self-attention mechanism of Transformer is used to learn the global representation of the image from the multi-layer features extracted from different levels of CNN. Additionally, to forecast the ultimate image quality score, the global and local attributes are combined. Finally, the proposed algorithm is evaluated on three public datasets commonly used in CSIQ [1], TID2013 [2] and LIVE [3]. Compared with eight traditional no-reference image quality evaluation algorithms and five methods based on deep learning, the proposed algorithm achieves competitive results. © 2023 IEEE.

关键词： Convolutional neural networks

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Optical and Electrical Analysis of an Advanced 4T All-perovskite Tandem Solar Cell

Optical and Electrical Analysis of an Advanced 4T All-perovs...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Zhaosheng Xia Xingang Ren Shengyang Wei Zhixiang Huang Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

The investigation of the optoelectronic characteristics of all-perovskite tandem solar cells holds pivotal significance in surpassing the Shockley-Queisser limit of single-junction perovskite solar cells. Initially, we established two distinct four-terminal tandem solar cells (4T TSCs) composed of different perovskite materials: FA 0.8 Cs 0.2 Pb(I 0.7 Br 0.3 ) 3 (E g = 1.75 eV) and MASnI 3 (E g = 1.3 eV). Through numerical simulations of the optoelectronic characteristics of 4T TSCs, we evaluated the performance of the TSCs at varying perovskite layer thicknesses. Following optimization, the power conversion efficiency (PCE) of the 4T TSCs increased from the initial 30.34% to 33.54%. Through an in-depth analysis of the cell and energy distribution in TSCs, it was observed that the light-coupling layer significantly influenced the PCE of the 4T TSCs. Upon optimizing the light-coupling layer, the PCE of the TSC increased from the initial 30.34% to 34.7%. This study provides crucial theoretical and experimental foundations for optimizing the performance of all-perovskite TSCs.

关键词： Photovoltaic cells Numerical simulation Perovskites Optical coupling Optical materials Power conversion Electromagnetics

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Speaker Recognition Based on Pre-Trained Model and Deep Clustering

Speaker Recognition Based on Pre-Trained Model and Deep Clus...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang He Zhida Song Shuanghong Liu Mengqi Niu Ying Hu Hao Huang School of Computer Science and Technology Xinjiang University Urumqi China School of Intelligence 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

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In this paper, we propose a novel loss by integrating a deep clustering (DC) loss at the frame-level and a speaker recognition loss at the segment-level into a single network without additional data requirements and exhaustive computation. The DC loss implicitly generates soft pseudo-phoneme labels for each frame-level feature, which facilitates extracting more discriminant speaker representation by suppressing phonetic content information. We study the DC loss not only on the acoustic feature, but also on the features extracted by the pre-trained models, such as wav2vec 2.0, HuBERT and WavLM. Experimental results on the VoxCeleb dataset shows that the overall system performance based on the pre-trained model features are better than the one on the acoustic feature. The proposed loss is significantly effective for systems on the acoustic feature and has a marginal improvement for systems on the pre-trained model feature.

关键词： Training Costs Computational modeling System performance Phonetics Multimedia databases Feature extraction

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Digital image biological detection technology based on the porous silicon periodic crystals film

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Optoelectronics Letters 2021年第9期17卷 552-557页

作者： YANG Jianfeng JIA Zhenhong LüXiaoyi HUANG Xiaohui WANG Jiajia School of Physical Science and Technology Xinjiang UniversityUrumqi 830046China School of Information Science and Engineering Xinjiang UniversityUrumqi 830046China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous RegionXinjiang UniversityUrumqi 830046China

In order to solve this problem,a new biological detection method is proposed,which makes use of the characteristics of optical transmission at the edge of the spectral band gap and sensitive to refractive index *** the probe light with wavelength at the edge of the Bragg band gap of porous silicon is incident on the surface of porous silicon,the change of refractive index caused by deoxyribonucleic acid(DNA)reaction in porous silicon will affect the detection light intensity transmitted from the porous silicon *** analyzing the change of the average gray value of the transmitted light image,the concentration of the DNA can be obtained.

关键词： porous image refractive

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Extended State Observer Based Fault-Tolerant Predictive Control for a Four-Mecanum Wheels Mobile Robot 14

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

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14th IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Yang, Binghao Wang, Dongliang Wen, Ziling Wei, Wu Li, Wenji Fan, Zhun Shantou University Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong 515063 China Shantou University School of Department of Electronic and Information Engineering Guangdong 515063 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Guangzhou510640 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9798331506056

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. © 2024 IEEE.

关键词： Mobile robots

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