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Stable Extended U-Net for Noise-Robust Speaker Verification

Stable Extended U-Net for Noise-Robust Speaker Verification

引用

2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

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 Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798350368741

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. Researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model's performance improvement. Research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through sub-pixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness. © 2025 IEEE.

关键词： noisy environments out-of-domain generalization Speaker verification stable learning

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Generative Negative Sample Enhancement Based Few-Shot Named Entity Recognition 11th

Generative Negative Sample Enhancement Based Few-Shot Named ...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Duan, Zhen Xiao, Shenghua Chen, Jie Zhao, Shu Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei 230601 China School of Computer Science and Technology Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei 230601 China

ISBN: (纸本)9783031927409

Few-shot Named Entity Recognition (Few-shot NER) aims to identify and classify unseen named entity types with a limited labeled samples. In recent years, large language models have achieved remarkable results in various NLP tasks due to their excellent zero/few-shot generalization capabilities. Recent research has extensively focused on using large language models to study the named entity recognition task. Negative sample entities have been demonstrated to be effective when performing this task using in-context learning paradigm. However, the problem of low efficiency and poor quality in manually constructing negative sample entities has been exposed. To address this issue, in this paper, we propose a Few-shot NER method based on generative negative sample enhancement (GNSE). Specifically, GNSE first utilizes a large language model to pre-identify potential entity spans in the support set text paragraphs in a zero-shot manner and generate the semantics of the spans based on the paragraph context. Then, the pre-identified candidate spans are compared with the ground-truth labels to determine positive or negative sample entity spans. Finally, based on Chain-of-Thought, the span semantics, positive and negative sample entities, and ground-truth labels are inserted into the example template for the in-context learning paradigm, and the test text from the query set is concatenated to construct a complete prompt. GNSE was evaluated on three widely used datasets: CoNLL’03, WNUT’17 and GUM. Experiments show that GNSE has achieved better performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Few-shot Named Entity Recognition In-context Learning Large Language Model Prompt Engineering

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Joint Image Restoration For Domain Adaptive Object Detection In Foggy Weather Condition

Joint Image Restoration For Domain Adaptive Object Detection...

引用

IEEE International Conference on Image processing

作者： Jing Ma Meng Lin Gang Zhou Zhenhong Jia School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Driven by deep learning, object detection methods have made significant progress in recent years. However, there is still a domain shift between synthetic foggy data and real foggy data, this leads to a undesirable decrease in detection results when applying the algorithm model trained on synthetic foggy datasets. In this article, we design a domain-adaptive YOLOX object detection algorithm by joint image restoration, in order to improve object detection performance in foggy scenes. Specially, we design an end-to-end domain adaptive framework that combines dehazing module and YOLOX together. To achieve feature alignment, we introduced a domain classifier in the feature spaceand discuss its optimal placement in the framework. Experimental results show that the proposed domain adaptation method achieved 62.60 percent mAP on real foggy image datasets RTTs, outperforming other state-of-the-art methods.

关键词： Deep learning Adaptation models Object detection Feature extraction Data models Image restoration Classification algorithms

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Research on patent classification based on hierarchical label semantics 3

Research on patent classification based on hierarchical labe...

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3rd International Conference on Education, Knowledge and Information Management, ICEKIM 2022

作者： Chen, Jie Zhao, Shu Lu, Fan Liu, Feng Zhang, Yangping Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9781665416665

Patent classification is an essential task in patent information management and knowledge mining. Most existing studies are based on the textual content of individual patent texts (e.g., titles and abstracts) for classification, but the patent also has label information with hierarchical structure and semantic description. However, the semantics correlation of patent texts and their labels have been largely ignored. In this paper, we propose a novel framework called HLSPC for patent classification by leveraging the hierarchical semantics correlation of patent texts and their labels. Specifically, we first apply a patent representation learning module for capturing the semantics representation of patent texts and hierarchical labels. Then, we design a label attention learning module to build the semantics correlation between patent texts and hierarchical labels, which enhances patent representation. Finally, we deploy a multi-level fusion module to get the refined category prediction for each patent which can preserve both local and global hierarchical prediction information. Extensive experimental results on two English patent datasets demonstrate the effective power of the HLSPC model. © 2022 IEEE.

关键词： Semantics

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Attention-based Dual-Branch Network for Micro-Expression Recognition with Global-Local Feature Fusion

Attention-based Dual-Branch Network for Micro-Expression Rec...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Yupeng Qi Mayire Ibrayim Askar Hamdulla School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Micro-expression(ME) is an uncontrollable muscle movement that appears on the face when people try to hide or inhibit their real emotions, which has the problems of short duration, small movement amplitude and uneven distribution. In order to solve the problem of localization and asymmetry in the distribution of ME features, this paper proposes a new two-branch attention network to recognize MEs, which utilizes the attention mechanism to capture global and local ME features. The network is mainly divided into three parts: data preprocessing, ME feature learning and feature fusion classification. First, the data preprocessing first extracts the ME optical flow features and then divides them into four regions, which are used as inputs to the two-branch network respectively. Second, the two-branch network uses the Inception-MSFE global multi-scale feature extraction network incorporating the attention mechanism (CBAM) and the Swin Transformer-based local feature extraction network for feature learning, respectively. Finally, MEs are predicted by fusing global and local features of MEs. Experimental validation is carried out on three datasets, CASME II, SAMM, and SMIC, which proves that Acc, UAR, and UF1 are 0.797, 0.702, and 0.698 on the SAMM dataset, respectively; Acc, UAR, and UF1 are 0.734, 0.723, and 0.729 on SMIC, respectively; and on the CASME II dataset, Acc, UAR and UF1 are 0.865, 0.872, and 0.889, respectively, which are competitive with other state-of-the-art methods.

关键词： Representation learning Attention mechanisms Computational modeling Data preprocessing Training data Feature extraction Generative adversarial networks Transformers Data mining Optical flow

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SAMNER: Image Screening and Cross-Modal Alignment Networks for Multimodal Named Entity Recognition

SAMNER: Image Screening and Cross-Modal Alignment Networks f...

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International Joint Conference on Neural Networks (IJCNN)

作者： Luyi Yang School of Computer Science and Technology Xinjiang University Xinjiang China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the proliferation of social media data, Multimodal Named Entity Recognition (MNER) has received much attention; using different data modalities is crucial for the development of natural language processing and neural networks. However, existing methods suffer from two drawbacks: 1) text-image pairs in the data only sometimes correspond to each other, and it is impossible to rely on contextual information due to the short text nature of social media. 2) Despite the introduction of visual information, heterogeneity gaps may occur in previous complex fusion methods, leading to misidentification. This paper proposes a new synthetic image with a selected graphic alignment network(SAMNER) to address these challenges and construct a matching relationship between external images and text. To solve the graphic mismatch problem, we use a stable diffusion model to generate the images and perform entity labeling. Specifically, we generate images and perform entity labeling through the stable diffusion model to generate the image with the highest match to the text, filter the generated images by the internal image set to generate the best image, and then perform multimodal fusion to predict the entity labeling, we design a simple and effective multimodal attentional alignment mechanism to obtain a better visual representation, and we conduct a large number of experiments. The experiments prove that our model produces competitive results on the two publicly available datasets.

关键词： Visualization Matched filters Image recognition Social networking (online) Text recognition Neural networks Named entity recognition

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On Cognitive radar Jamming for countering Cognitive Electromagnetic Protection systems

On Cognitive Radar Jamming for countering Cognitive Electrom...

引用

Technologies for Defense and Security (TechDefense), IEEE International Workshop on

作者： Hemanga Banerjee Christos Ilioudis Carmine Clemente Chris Williams Centre for Signal and Image Processing University of Strathclyde Glasgow Defence Science and Technology Laboratory Porton Down Wiltshire United Kingdom

ISBN: (数字)9798331505585

ISBN: (纸本)9798331505592

The paper introduces a novel model of a fully adaptive cognitive radar jamming approach, designed for the purpose of countering modern cognitive radars. Cognitive radar systems have been widely used in modern electromagnetic warfare and also serve as an effective counter to traditional jamming techniques. Cognitive jamming makes use of machine learning and adaptive signal processing strategies to dynamically adapt jamming strategies in response to real-time changes in target radar transmissions. The proposed model may be used to incorporate advanced reinforcement learning algorithms, signal feature extraction techniques, and waveform optimization techniques to create a cohesive system that can optimize jamming strategies with minimal prior knowledge of the electromagnetic environment. The paper also discusses the state-of-the-art in cognitive radar jamming technologies, and their efficiency in the face of intelligent radar equipped with advanced anti-jamming measures. Furthermore, it addresses current challenges and future directions in the field with considerations for defense and security applications.

关键词： Electromagnetic scattering signal processing algorithms Cognitive radar Feature extraction Real-time systems Security Jamming Electromagnetics Protection Optimization

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Multi-branch Network with Cross-Domain Feature Fusion for Anomalous Sound Detection 1

引用

18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Fang, Wenjie Fan, Xin Hu, Ying School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of signal detection and processing in Xinjiang Urumqi China

ISBN: (数字)9789819706013

ISBN: (纸本)9789819706006

Anomalous sound detection (ASD) is a key technology to identify abnormal sounds in various industries. Self-supervised anomalous sound detection aims at detecting unknown machine anomalous sounds by learning the characteristics of the normal sounds using metainformation. In this paper, we propose a multi-branch network with cross-domain feature fusion (MBN-CFF) for self-supervised ASD task. The multi-branch network splits the complete feature representations and feeds them individually into classifiers to generate category predictions. The weighted loss, calculated by multiple predictions and the real labels, guides the model training process. We also design a cross-domain feature fusion (CFF) block for effectively fusing the time-domain and frequency-domain features and an attentive sandglass (AS) block for effectively extracting features. Experimental results on the DCASE2020 challenge task 2 show that our MBN-CFF network achieves the best performance with the AUC score of 94.73% and pAUC score of 88.74%, respectively, compared to the other five existing methods for anomalous sound detection. The results of ablation experiments show the effectiveness of CFF and AS blocks, multi-brach prediction (MBP). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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A Beamspace Dimension Reduction Technique with Application to DOA estimation in Low-angle Tracking

A Beamspace Dimension Reduction Technique with Application t...

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2021 CIE International Conference on radar, radar 2021

作者： Xu, Saiqin Chen, Baixiao Chen, Xiaoying Xiang, Houhong Xidian University National Laboratory of Radar Signal Processing Xi'an China School of Computer and Information Hefei University of Technology Hefei China

ISBN: (纸本)9781665498142

We develop a dimension reduction algorithm that against coherent signals with application to direction of arrival (DOA) estimation in low-angle tracking. The dimension reduction is done as a linear transformation which is commonly referred to as beamspace (BS). In this paper, the BS covariance matrix is reconstituted from several beams which can be applied to synthetic vector maximum likelihood (SVML) estimator. The proposed method is computationally less expensive to obtain similar DOA estimation performance as compared to the existing element space (ES) technique. Simulation results and field data sets validate the robustness of the developed algorithm, and they show its effectiveness and superiority to the ES SVML algorithm. © 2021 IEEE.

关键词： beamspace transformation coherent signals DOA estimation synthetic vector maximum likelihood

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WavLM and Omni-Scale CNNs: Enhancing Boundary Detection in Partially Spoofed Audio

WavLM and Omni-Scale CNNs: Enhancing Boundary Detection in P...

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Asia-Pacific signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Menghan Li Zhihua Huang School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing in Xinjiang Urumqi China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Partially spoofed/fake audio, in which segments of utterances are replaced with synthetic or natural audio clips, has emerged as a new form of deep audio forgery, posing potential severe threats to societal security. To address this issue, we employ a deep learning-based frame-level detection system, introducing a frame-level detection approach. We explore the effectiveness of WavLM in waveform boundary detection, utilizing WavLM as a feature extractor for original audio samples. Acoustic features and frame-level embeddings are concatenated, with an OS block embedded within the frame-level feature extraction process, in conjunction with CNN-1D to form the ResNet-OS network. This system can detect partially deceived audio and pinpoint the manipulated segments, effectively integrating multi-scale convolution to consider the interplay between local and global information in time series classification tasks. Experimental results show that this approach offers substantial generalization capabilities and robustness compared to traditional frame-level detection techniques.

关键词： Convolution Time series analysis Asia Information processing Feature extraction Acoustics Robustness Forgery Security

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