IEEE Signal Processing Letters最新文献

Converting Interference to Gain: Enhancing Sensing Capabilities of ISAC Systems via Noncooperative Base Station Signals 将干扰转换为增益：通过非合作基站信号增强ISAC系统的感知能力

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-15 DOI: 10.1109/LSP.2025.3560904

Zehua Yu;Haibo Zhao;Qinghua Guo;Jinshan Ding

引用次数: 0

Progressive Skip Connection Improves Consistency of Diffusion-Based Speech Enhancement 渐进式跳跃连接提高了基于扩散的语音增强的一致性

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-14 DOI: 10.1109/LSP.2025.3560622

Yue Lei;Xucheng Luo;Wenxin Tai;Fan Zhou

{"title":"Progressive Skip Connection Improves Consistency of Diffusion-Based Speech Enhancement","authors":"Yue Lei;Xucheng Luo;Wenxin Tai;Fan Zhou","doi":"10.1109/LSP.2025.3560622","DOIUrl":"https://doi.org/10.1109/LSP.2025.3560622","url":null,"abstract":"Recent advancements in generative modeling have successfully integrated denoising diffusion probabilistic models (DDPMs) into the domain of speech enhancement (SE). Despite their considerable advantages in generalizability, ensuring semantic consistency of the generated samples with the condition signal remains a formidable challenge. Inspired by techniques addressing posterior collapse in variational autoencoders, we explore skip connections within diffusion-based SE models to improve consistency with condition signals. However, experiments reveal that simply adding skip connections is ineffective and even counterproductive. We argue that the independence between the predictive target and the condition signal causes this failure. To address this, we modify the training objective from predicting random Gaussian noise to predicting clean speech and propose a progressive skip connection strategy to mitigate the decrease in mutual information between the layer's output and the condition signal as network depth increases. Experiments on two standard datasets demonstrate the effectiveness of our approach in both seen and unseen scenarios.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1650-1654"},"PeriodicalIF":3.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865337","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

A Streamable Neural Audio Codec With Residual Scalar-Vector Quantization for Real-Time Communication 基于残差标量矢量量化的可流神经音频编解码器

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-11 DOI: 10.1109/LSP.2025.3560172

Xiao-Hang Jiang;Yang Ai;Rui-Chen Zheng;Zhen-Hua Ling

{"title":"A Streamable Neural Audio Codec With Residual Scalar-Vector Quantization for Real-Time Communication","authors":"Xiao-Hang Jiang;Yang Ai;Rui-Chen Zheng;Zhen-Hua Ling","doi":"10.1109/LSP.2025.3560172","DOIUrl":"https://doi.org/10.1109/LSP.2025.3560172","url":null,"abstract":"This paper proposes StreamCodec, a streamable neural audio codec designed for real-time communication. StreamCodec adopts a fully causal, symmetric encoder-decoder structure and operates in the modified discrete cosine transform (MDCT) domain, aiming for low-latency inference and real-time efficient generation. To improve codebook utilization efficiency and compensate for the audio quality loss caused by structural causality, StreamCodec introduces a novel residual scalar-vector quantizer (RSVQ). The RSVQ sequentially connects scalar quantizers and improved vector quantizers in a residual manner, constructing coarse audio contours and refining acoustic details, respectively. Experimental results confirm that the proposed StreamCodec achieves decoded audio quality comparable to advanced non-streamable neural audio codecs. Specifically, on the 16 kHz LibriTTS dataset, StreamCodec attains a ViSQOL score of 4.30 at 1.5 kbps. It has a fixed latency of only 20 ms and achieves a generation speed nearly 20 times real-time on a CPU, with a lightweight model size of just 7 M parameters, making it highly suitable for real-time communication applications.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1645-1649"},"PeriodicalIF":3.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865336","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

Image Forgery Localization With State Space Models 利用状态空间模型进行图像伪造定位

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-09 DOI: 10.1109/LSP.2025.3559429

Zijie Lou;Gang Cao;Kun Guo;Shaowei Weng;Lifang Yu

{"title":"Image Forgery Localization With State Space Models","authors":"Zijie Lou;Gang Cao;Kun Guo;Shaowei Weng;Lifang Yu","doi":"10.1109/LSP.2025.3559429","DOIUrl":"https://doi.org/10.1109/LSP.2025.3559429","url":null,"abstract":"Pixel dependency modeling from tampered images is pivotal for image forgery localization. Current approaches predominantly rely on Convolutional Neural Networks (CNNs) or Transformer-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. They not only excel in modeling long-range interactions but also maintain a linear computational complexity. In this paper, we propose LoMa, a novel image forgery localization method that leverages the selective SSMs. Specifically, LoMa initially employs atrous selective scan to traverse the spatial domain and convert the tampered image into ordered patch sequences, and subsequently applies multi-directional state space modeling. In addition, an auxiliary convolutional branch is introduced to enhance local feature extraction. Extensive experimental results validate the superiority of LoMa over CNN-based and Transformer-based state-of-the-arts. To our best knowledge, this is the first image forgery localization model constructed based on the SSM-based model. We aim to establish a baseline and provide valuable insights for the future development of more efficient and effective SSM-based forgery localization models.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1590-1594"},"PeriodicalIF":3.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856330","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

Self-Weighted Multi-View Fuzzy Clustering With Multiple Graph Learning 基于多图学习的自加权多视图模糊聚类

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-08 DOI: 10.1109/LSP.2025.3558161

Chaodie Liu;Cheng Chang;Feiping Nie

{"title":"Self-Weighted Multi-View Fuzzy Clustering With Multiple Graph Learning","authors":"Chaodie Liu;Cheng Chang;Feiping Nie","doi":"10.1109/LSP.2025.3558161","DOIUrl":"https://doi.org/10.1109/LSP.2025.3558161","url":null,"abstract":"Graph-based multi-view clustering has garnered considerable attention owing to its effectiveness. Nevertheless, despite the promising performance achieved by previous studies, several limitations remain to be addressed. Most graph-based models employ a two-stage strategy involving relaxation and discretization to derive clustering results, which may lead to deviation from the original problem. Moreover, graph-based methods do not adequately address the challenges of overlapping clusters or ambiguous cluster membership. Additionally, assigning appropriate weights based on the importance of each view is crucial. To address these problems, we propose a self-weighted multi-view fuzzy clustering algorithm that incorporates multiple graph learning. Specifically, we automatically allocate weights corresponding to each view to construct a fused similarity graph matrix. Subsequently, we approximate it as the scaled product of fuzzy membership matrices to directly derive clustering assignments. An iterative optimization algorithm is designed for solving the proposed model. Experiment evaluations conducted on benchmark datasets illustrate that the proposed method outperforms several leading multi-view clustering approaches.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1585-1589"},"PeriodicalIF":3.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856264","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

Clustering-Based Adaptive Query Generation for Semantic Segmentation 基于聚类的语义分割自适应查询生成

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-08 DOI: 10.1109/LSP.2025.3558160

Yeong Woo Kim;Wonjun Kim

{"title":"Clustering-Based Adaptive Query Generation for Semantic Segmentation","authors":"Yeong Woo Kim;Wonjun Kim","doi":"10.1109/LSP.2025.3558160","DOIUrl":"https://doi.org/10.1109/LSP.2025.3558160","url":null,"abstract":"Semantic segmentation is one of the crucial tasks in the field of computer vision, aiming to label each pixel according to its class. Most recently, several semantic segmentation methods, which adopt the transformer decoder with learnable queries, have achieved the impressive improvement. However, since learnable queries are primarily determined by the distribution of training samples, discriminative characteristics of the input image often have been disregarded. In this letter, we propose a novel clustering-based query generation method for semantic segmentation. The key idea of the proposed method is to adaptively generate queries based on the clustering scheme, which leverages semantic affinities in the latent space. By aggregating latent features that represent the same class in a given input, the semantic information of each class can be efficiently encoded into the query. Furthermore, we propose to apply the auxiliary loss function to predict the segmentation result in a coarse scale during the process of query generation. This enables each query to grasp spatial information of the target object in a given image. Experimental results on various benchmarks show that the proposed method effectively improves the performance of semantic segmentation.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1580-1584"},"PeriodicalIF":3.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856346","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

Improved Encoder-Decoder Architecture With Human-Like Perception Attention for Monaural Speech Enhancement 基于类人感知注意力的单耳语音增强改进编码器-解码器结构

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-07 DOI: 10.1109/LSP.2025.3558690

Hao Zhou;Yi Zhou;Zhenhua Cheng;Yu Zhao;Yin Liu

{"title":"Improved Encoder-Decoder Architecture With Human-Like Perception Attention for Monaural Speech Enhancement","authors":"Hao Zhou;Yi Zhou;Zhenhua Cheng;Yu Zhao;Yin Liu","doi":"10.1109/LSP.2025.3558690","DOIUrl":"https://doi.org/10.1109/LSP.2025.3558690","url":null,"abstract":"Speech enhancement (SE) models based on deep neural networks (DNNs) have shown excellent denoising performance. However, mainstream SE models often have high structural complexity and large parameter sizes, requiring substantial computational resources, which limits their practical application. In this paper, a high-efficiency encoder-decoder structure, inspired by the top-down attention mechanism in human brain perception and named human-like perception attention network (HPANet), is proposed for monaural speech enhancement, which is able to emulate brain perceptual attention in noise environments. In HPANet, the raw waveform is first encoded by using attention encoder to capture shallow global features. These features are then downsampled, and multi-scale information is aggregated through top attention module to prevent the loss of crucial information. Next, down attention module integrates features from neighboring layers to reconstruct signal in a top-down manner. Finally, the decoder reconstructs the denoised clean signal. Experiments show that the proposed method effectively reduces model complexity while maintaining competitive performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1670-1674"},"PeriodicalIF":3.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883430","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

Nonlinear Schur-Type Audio Signal Parameterization for Convolutional Networks 卷积网络的非线性schur型音频信号参数化

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-07 DOI: 10.1109/LSP.2025.3558689

Pawel Biernacki;Urszula Libal

引用次数: 0

DSDNet: Target Detection Algorithm for SDSS Photometric Images Based on Convolutional Neural Networks 基于卷积神经网络的SDSS测光图像目标检测算法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-04 DOI: 10.1109/LSP.2025.3558115

Ziyi Zhang;Bo Qiu;Xia Jiang;Ali Luo;Fuji Ren

{"title":"DSDNet: Target Detection Algorithm for SDSS Photometric Images Based on Convolutional Neural Networks","authors":"Ziyi Zhang;Bo Qiu;Xia Jiang;Ali Luo;Fuji Ren","doi":"10.1109/LSP.2025.3558115","DOIUrl":"https://doi.org/10.1109/LSP.2025.3558115","url":null,"abstract":"The Sloan Digital Sky Survey (SDSS), a large-scale astronomical survey project, has released a vast volume of photometric images. These images play a pivotal role in deriving fundamental parameters of celestial objects and investigating the structure of the universe. Nevertheless, in dense star fields, the characteristics of celestial sources are intricate, rendering traditional methods incapable of conducting precise analyses. To address this challenge, this paper introduces a new algorithm named DSDNet for detecting celestial sources in dense star fields and counting them. During the feature extraction phase, DSDNet generates larger feature maps, thereby preserving more information about small targets. By incorporating a convolutional attention module, the model's capacity to learn the features of celestial sources in dense star fields is augmented. Furthermore, to more effectively manage the blending phenomenon among sources, DSDNet integrates CNN and Transformer architectures, enhancing the model's ability to comprehend global features. Experimental findings show that DSDNet exhibits excellent performance, attaining an F1-score of 97.23%. This makes it a valuable resource for aiding SDSS in processing dense star field images.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1660-1664"},"PeriodicalIF":3.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883432","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

Optimization for Paralyzing G2A Communication Network: A DRL-Based Joint Path Planning and Jamming Power Allocation Approach 瘫痪G2A通信网络优化：基于drl的联合路径规划与干扰功率分配方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-04 DOI: 10.1109/LSP.2025.3558123

Xiang Peng;Hua Xu;Zisen Qi;Dan Wang;Yiqiong Pang

{"title":"Optimization for Paralyzing G2A Communication Network: A DRL-Based Joint Path Planning and Jamming Power Allocation Approach","authors":"Xiang Peng;Hua Xu;Zisen Qi;Dan Wang;Yiqiong Pang","doi":"10.1109/LSP.2025.3558123","DOIUrl":"https://doi.org/10.1109/LSP.2025.3558123","url":null,"abstract":"This letter investigates the jammer path planning and jamming power allocation problem during airborne deterrence operation (ADO) in highly dynamic environments. In response to airborne threats posed by enemy aircraft formations, jammers must rely on perceptual information to plan trajectories and emit jamming signals to paralyze the ground-to-air (G2A) communication networks. Unlike traditional static scenarios, the high mobility of both sides presents significant challenges. Most works only study jamming solutions for static ground or single airborne targets, failing to address multiple airborne targets. We propose a joint path planning and jamming power allocation approach based on deep reinforcement learning (JPPJPA-DRL). This approach considers the impact of flight paths on receiving antenna gain, models the ADO as a Markov Decision Process (MDP), and uses the proximal policy optimization (PPO) algorithm to generate optimized path points and jamming power allocation schemes. In addition, a scientific reward function is designed to guide the learning process, and a visual communication countermeasure simulation platform is developed. The results show that the proposed approach can efficiently paralyze G2A communication networks, outperforming the baseline.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1640-1644"},"PeriodicalIF":3.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865269","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