IEEE Signal Processing Letters最新文献_第8页

Ambiguity-Free Broadband DOA Estimation Relying on Parameterized Time-Frequency Transform 基于参数化时频变换的无模糊宽带DOA估计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-10 DOI: 10.1109/LSP.2025.3550002

Wei Wang;Shefeng Yan;Linlin Mao;Zeping Sui;Jirui Yang

引用次数: 0

Contextual Direct Position Determination for Path Loss Informed Localization 基于路径损失的上下文直接定位

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-10 DOI: 10.1109/LSP.2025.3550047

Licheng Zhao;Wenqiang Pu;Rui Zhou;Ming-Yi You;Qingjiang Shi

引用次数: 0

Adaptive Alignment and Time Aggregation Network for Speech-Visual Emotion Recognition 语音-视觉情感识别的自适应对齐和时间聚合网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-10 DOI: 10.1109/LSP.2025.3550007

Lile Wu;Lei Bai;Wenhao Cheng;Zutian Cheng;Guanghui Chen

引用次数: 0

Efficient Feature Focus Enhanced Network for Small and Dense Object Detection in SAR Images 高效特征聚焦增强网络用于SAR图像的小密度目标检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-07 DOI: 10.1109/LSP.2025.3548934

Cong Li;Lihu Xi;Yongqiang Hei;Wentao Li;Zhu Xiao

引用次数: 0

DUAL-GDFQ: A Dual-Generator, Dual-Phase Learning Approach for Data-Free Quantization DUAL-GDFQ：一种用于无数据量化的双发生器、双相位学习方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-07 DOI: 10.1109/LSP.2025.3549025

Sihan Wang;Zhi Han;Xiyao Liu

引用次数: 0

Learning Source-Free Domain Adaptation for Infrared Small Target Detection 学习无源域自适应红外小目标检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-07 DOI: 10.1109/LSP.2025.3549000

Hongxu Jin;Baiyang Chen;Qianwen Lu;Qingchuan Tao;Yongxiang Li

{"title":"Learning Source-Free Domain Adaptation for Infrared Small Target Detection","authors":"Hongxu Jin;Baiyang Chen;Qianwen Lu;Qingchuan Tao;Yongxiang Li","doi":"10.1109/LSP.2025.3549000","DOIUrl":"https://doi.org/10.1109/LSP.2025.3549000","url":null,"abstract":"Existing infrared small target detection (IRSTD) methods mainly rely on the assumption that the training and testing data come from the same distribution, a premise that does not hold in many real-world scenarios. Additionally, the inability to access source domain data in numerous IRSTD tasks further complicates the domain adaptation process. To address these challenges, we propose a novel Source-Free Domain Adaptation (SFDA) framework for IRSTD, denoted as IRSTD-SFDA. This framework comprises two key components: Multi-expert Domain Adaptation (MDA) and Multi-scale Focused Learning (MFL). MDA leverages the source model to generate pseudo masks for the target domain, facilitating the transfer of knowledge from the source to the target domain. To account for the inherent diversity of small targets across domains, MDA refines these pseudo masks through a series of operations, including target localization, rolling guidance filtering, shape adaptation, and multi-expert decision, thereby mitigating morphological discrepancies between the source and target domains. Meanwhile, MFL employs a global-local fusion strategy to focus on critical regions, enhancing the model's ability to detect small infrared targets. Extensive experimental evaluations across various cross-domain scenarios demonstrate the effectiveness of the proposed framework.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1121-1125"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heterogeneous Domain Remapping for Universal Detection of Generative Linguistic Steganography 基于异构域映射的生成语言隐写通用检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-07 DOI: 10.1109/LSP.2025.3549015

Tong Xiao;Jingang Wang;Songbin Li

{"title":"Heterogeneous Domain Remapping for Universal Detection of Generative Linguistic Steganography","authors":"Tong Xiao;Jingang Wang;Songbin Li","doi":"10.1109/LSP.2025.3549015","DOIUrl":"https://doi.org/10.1109/LSP.2025.3549015","url":null,"abstract":"Current researchers have proposed various steganalysis methods for detecting secret information within social media texts, which can achieve relatively optimal detection performance in specific steganographic domains. However, considering the practical application of social media, we can only obtain the text to be tested without prior knowledge of the steganographic domain it belongs to. Consequently, we are unable to prepare a supervised training dataset in advance. This places higher demands on steganalysis algorithms, necessitating their ability to generalize and detect any unknown steganography domain. To this end, we propose a universal detection method for generative linguistic steganography based on heterogeneous domain remapping. The core idea is to employ a neural structure composed of pre-trained embedding layers and capsule networks to extract steganography-sensitive correlation features. Subsequently, the concept of contrastive learning is utilized to remap the sensitive features from heterogeneous steganography domains into a unified domain. This process effectively extracts domain-invariant features, thereby enabling the detection of unknown steganographic domains. Experimental results demonstrate that the proposed method outperforms existing approaches by an average of over 2% across various steganography domains.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1281-1285"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spectral Scaling-Based Augmentation for Corruption-Robust Image Classification 基于谱标度的鲁棒图像分类方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-07 DOI: 10.1109/LSP.2025.3549014

Zhuang Zhang;Lijun Zhang;Dejian Meng;Wei Tian;Jun Yan

{"title":"Spectral Scaling-Based Augmentation for Corruption-Robust Image Classification","authors":"Zhuang Zhang;Lijun Zhang;Dejian Meng;Wei Tian;Jun Yan","doi":"10.1109/LSP.2025.3549014","DOIUrl":"https://doi.org/10.1109/LSP.2025.3549014","url":null,"abstract":"Image classifiers often degrade in performance when test images differ significantly from the training distribution due to real-world image corruptions. Frequency-based augmentations can be used to address this issue, but existing methods excel against corruptions caused by noise and blur while struggling with those caused by contrast and fog. To tackle these challenges, we propose a novel image augmentation method grounded in a new perspective of relative spectral differences. This perspective characterizes spectral variations introduced by common corruptions as changes in non-zero frequencies, providing a unified understanding of their effects on image spectra. Building on this insight, the proposed method incorporates two key modules: a random spectral scaling module that captures statistical properties of image spectra and a deep spectral scaling module that adaptively learns spectral adjustments through a neural network. Experiments demonstrate that the proposed method improves overall robustness across various corruptions, with notable gains of 6.3% and 6.4% on contrast and fog, respectively, where existing methods often fall short.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1206-1210"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Bird Vocalization Recognition in Open-Set Cross-Corpus Scenarios With Semantic Feature Reconstruction and Dual Strategy Scoring 基于语义特征重构和双重策略评分的开放集跨语料库场景下鸟类发声识别

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-07 DOI: 10.1109/LSP.2025.3549008

Jiangjian Xie;Yingqi Wang;Xinyuan Qian;Junguo Zhang;Björn W. Schuller

{"title":"Improving Bird Vocalization Recognition in Open-Set Cross-Corpus Scenarios With Semantic Feature Reconstruction and Dual Strategy Scoring","authors":"Jiangjian Xie;Yingqi Wang;Xinyuan Qian;Junguo Zhang;Björn W. Schuller","doi":"10.1109/LSP.2025.3549008","DOIUrl":"https://doi.org/10.1109/LSP.2025.3549008","url":null,"abstract":"Automated recognition of bird vocalizations (BVs) is essential for biodiversity monitoring through passive acoustic monitoring (PAM), yet deep learning (DL) models encounter substantial challenges in open environments. These include difficulties in detecting unknown classes, extracting species-specific features, and achieving robust cross-corpus recognition. To address these challenges, this letter presents a DL-based open-set cross-corpus recognition method for BVs that combines feature construction with open-set recognition (OSR) techniques. We introduce a three-channel spectrogram that integrates both amplitude and phase information to enhance feature representation. To improve the recognition accuracy of known classes across corpora, we employ a class-specific semantic reconstruction model to extract deep features. For unknown class discrimination, we propose a Dual Strategy Coupling Scoring (DSCS) mechanism, which synthesizes the log-likelihood ratio score (LLRS) and reconstruction error score (RES). Our method achieves the highest weighted accuracy among existing approaches on a public dataset, demonstrating its effectiveness for open-set cross-corpus bird vocalization recognition.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1515-1519"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ProSegDiff: Prostate Segmentation Diffusion Network Based on Adaptive Adjustment of Injection Features ProSegDiff：基于注射特征自适应调整的前列腺分割扩散网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-06 DOI: 10.1109/LSP.2025.3548422

Jialong Zhong;Tingwei Liu;Yongri Piao;Weibing Sun;Huchuan Lu

引用次数: 0