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Causal Rank Lasso for Single Index Model
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-19 DOI: 10.1109/LSP.2025.3543742
Xin Shen;Jiyuan Tu;Feimeng Wang
{"title":"Causal Rank Lasso for Single Index Model","authors":"Xin Shen;Jiyuan Tu;Feimeng Wang","doi":"10.1109/LSP.2025.3543742","DOIUrl":"https://doi.org/10.1109/LSP.2025.3543742","url":null,"abstract":"This letter focuses on estimating the average treatment effect within a high-dimensional single-index model framework. We employ the recently introduced concept of the rank average treatment effect (rank-ATE) as an alternative measure for assessing differences in potential outcomes. To estimate both the rank-ATE and the model parameters simultaneously, we propose the causal rank Lasso estimator. Specifically, our method involves regressing the outcome rank on both the the treatment indicator and the covariates. We demonstrate that our estimator consistently identifies the direction and support of the true model parameter. Additionally, we introduced a novel irrepresentable condition to establish the support recovery in causal rank Lasso. Simulation studies are provided to validate the efficacy of our approach.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1061-1065"},"PeriodicalIF":3.2,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627849","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
Switching Games for Image Compression
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-18 DOI: 10.1109/LSP.2025.3543744
Marko Huhtanen
{"title":"Switching Games for Image Compression","authors":"Marko Huhtanen","doi":"10.1109/LSP.2025.3543744","DOIUrl":"https://doi.org/10.1109/LSP.2025.3543744","url":null,"abstract":"To compress an image, a technique based on optimal scalings with diagonal matrices is described. To start the process, an initial image of high compression ratio is required. Such an image can be produced, for example, with the 2D FFT or 2D DCT of the original image. Principal component analysis is a special case of this compression technique where the initial image is extremely rough, consisting of the first 2D Fourier basis function only. This initial image is then optimally orthogonalized and expanded by iteratively applying diagonal matrices from the left and right to attain double orthogonality. The process can be viewed as a continuous version of Berlekamp's switching game.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1016-1020"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10892035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Revisiting Frequency-Invariant Beamformer Design Using Weighted Spatial Response Variation
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-18 DOI: 10.1109/LSP.2025.3543746
Lingxin Wang;Congwei Feng;Huawei Chen
{"title":"Revisiting Frequency-Invariant Beamformer Design Using Weighted Spatial Response Variation","authors":"Lingxin Wang;Congwei Feng;Huawei Chen","doi":"10.1109/LSP.2025.3543746","DOIUrl":"https://doi.org/10.1109/LSP.2025.3543746","url":null,"abstract":"The spatial response variation (SRV) is widely employed in frequency-invariant (FI) beamformer design, thanks to the fact that it provides more design degrees of freedom to achieve better FI performance. Recently, the weighted-SRV, a generalized form of SRV, was proposed for the FI beamformer design. It is shown that the weighted-SRV-based design outperforms the SRV-based design with mainlobe ripple and sidelobe level being able to be precisely controlled. However, the approximation error of reference beampattern in the weighted-SRV design may lead to slow convergence or even failure to converge. To address the problem, this paper reformulates the constrained weighted-SRV cost function into an unconstrained form. Under the reformulated cost function, the closed-form solutions of the weighted-SRV's weighting function are theoretically derived, and then an FI-beamformer design approach is proposed. Simulation results demonstrate the superior performance of the proposed approach.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"991-995"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553417","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
Towards Accurate 3D Human Reconstruction: Segmentation-Based Supervision With Uncertainty Estimation
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-18 DOI: 10.1109/LSP.2025.3543317
Han Yu;Jingyi Wu;Ziming Wang;Wei Ni;Liang Song
{"title":"Towards Accurate 3D Human Reconstruction: Segmentation-Based Supervision With Uncertainty Estimation","authors":"Han Yu;Jingyi Wu;Ziming Wang;Wei Ni;Liang Song","doi":"10.1109/LSP.2025.3543317","DOIUrl":"https://doi.org/10.1109/LSP.2025.3543317","url":null,"abstract":"Human body reconstruction leveraging image information has become a critical task in the signal processing community. Due to the scarcity of high-quality 3D labels, existing methods often neglect the impact of body shape on the realism of the reconstruction. We argue that parameterized human models (such as SMPL) can control the reconstructed body shape through parameters, a feature that is underutilized in most reconstruction systems. Therefore, we design an end-to-end 3D parameterized human reconstruction system capable of real-time reconstruction of realistically shaped human models. To meet system requirements, we propose the Segmentation-based Supervision with Uncertainty Estimation (SSUE) framework, which innovatively employs body part segmentation as supervisory information and mitigates the adverse effects of segmentation noise through uncertainty estimation. Experimental results demonstrate improvements of 3.2% over the SOTA methods in body shape reconstruction accuracy and enhancements in the precision of limb extremities with our SSUE framework.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1036-1040"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594316","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
Distribution Metric Based $V$-Matrix Support Vector Machine
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-18 DOI: 10.1109/LSP.2025.3543266
Yiwei Song;Yuanhai Shao;Chunna Li
{"title":"Distribution Metric Based $V$-Matrix Support Vector Machine","authors":"Yiwei Song;Yuanhai Shao;Chunna Li","doi":"10.1109/LSP.2025.3543266","DOIUrl":"https://doi.org/10.1109/LSP.2025.3543266","url":null,"abstract":"The <inline-formula><tex-math>$V$</tex-math></inline-formula>-matrix Support Vector Machine (VSVM) is an innovative machine learning method recently proposed by Vapnik and Izmailov, which integrates positional relationships among training samples into the model learning, yielding the decision via conditional probability. But it overlooks the distribution information hidden in the data which plays a pivotal role in the training process and neglects the utilization of testing samples. To fully exploit the distribution information of the data, this paper proposes a novel Distribution Metric Based <inline-formula><tex-math>$V$</tex-math></inline-formula>-matrix Support Vector Machine (DVSVM) building upon VSVM. DVSVM incorporates the distributional information implicit in the data by measuring the distances between samples using the Wasserstein distance. Compared to VSVM, it also additionally accounts for the positional relationships of testing samples. It is further theoretically proved that VSVM can degenerate from DVSVM under certain conditions. Experimental results on several synthetic datasets and real-world disease datasets demonstrate the superiority of DVSVM.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1031-1035"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594369","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
UnitDiff: A Unit-Diffusion Model for Code-Switching Speech Synthesis
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-18 DOI: 10.1109/LSP.2025.3543456
Ke Chen;Zhihua Huang;Liang He;Yonghong Yan
{"title":"UnitDiff: A Unit-Diffusion Model for Code-Switching Speech Synthesis","authors":"Ke Chen;Zhihua Huang;Liang He;Yonghong Yan","doi":"10.1109/LSP.2025.3543456","DOIUrl":"https://doi.org/10.1109/LSP.2025.3543456","url":null,"abstract":"Given the scarcity of Code-Switching (CS) datasets, most researchers synthesize CS speech using multiple monolingual datasets. However, this approach presents challenges in synthesizing CS speech, such as difficulty controlling the speaker's identity and causing low intelligibility of the generated speech. This letter proposes UnitDiff, a CS speech synthesis model based on the unit-diffusion framework. The model employs the self-supervised high-level representation ’soft unit' extracted from soft HuBERT to directly predict a clean mel-spectrogram <inline-formula><tex-math>$x_{0}$</tex-math></inline-formula>. This approach enhances control over speaker identity. A language tagging method is also introduced to improve speech intelligibility. Evaluation results validate the model's effectiveness in improving the intelligibility, speaker similarity, and speaker consistency of the generated CS speech.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1051-1055"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629598","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
Signal-Domain Fully Coherent Accurate Measurement and Tracking of High-Speed Target
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-18 DOI: 10.1109/LSP.2025.3543316
Qinquan Zhou;Shaopeng Wei;Lei Zhang
{"title":"Signal-Domain Fully Coherent Accurate Measurement and Tracking of High-Speed Target","authors":"Qinquan Zhou;Shaopeng Wei;Lei Zhang","doi":"10.1109/LSP.2025.3543316","DOIUrl":"https://doi.org/10.1109/LSP.2025.3543316","url":null,"abstract":"The high speed and maneuverability of the spacecraft make the traditional wideband tracking algorithms fail to achieve echo coherent recovery under low SNR, resulting in reduced tracking accuracy. This paper proposes a fully coherent accurate measurement and tracking method based on the multi-channel hypothesis to realize the fully coherent recovery of dechirp echo and Doppler phase disambiguation. The algorithm performs initialization estimation and designs the multi-channel hypothesis velocity ambiguity interval. Then, it traverses the interval to perform the fully coherent accurate measurement and tracking to obtain the motion state update, employed to echo coherent recovery. Finally, the N/M criterion is introduced to evaluate the envelope alignment of the recovered echoes and filter out the unambiguous velocity. Experiments demonstrate its remarkable performance in low SNR conditions.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"986-990"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553429","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
Inference-Adaptive Steering of Neural Networks for Real-Time Area-Based Sound Source Separation
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-18 DOI: 10.1109/LSP.2025.3543454
Martin Strauss;Wolfgang Mack;María Luis Valero;Okan Köpüklü
{"title":"Inference-Adaptive Steering of Neural Networks for Real-Time Area-Based Sound Source Separation","authors":"Martin Strauss;Wolfgang Mack;María Luis Valero;Okan Köpüklü","doi":"10.1109/LSP.2025.3543454","DOIUrl":"https://doi.org/10.1109/LSP.2025.3543454","url":null,"abstract":"We propose a novel adaptive steering technique that changes the target area of a spatial-aware multi-microphone sound source separation algorithm during inference without the necessity of retraining the deep neural network (DNN). To achieve this, we first train a DNN aiming to retain speech within a target region, defined by an angular span, while suppressing sound sources stemming from other directions. Afterward, a phase shift is applied to the microphone signals, allowing us to shift the center of the target area during inference at negligible additional cost in computational complexity. Further, we show that the proposed approach performs well in a wide variety of acoustic scenarios, including several speakers inside and outside the target area and additional noise. More precisely, the proposed approach performs on par with DNNs trained explicitly for the steered target area in terms of DNSMOS and SI-SDR.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1041-1045"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594314","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
Follow the Approximate Sparse Leader for No-Regret Online Sparse Linear Approximation
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-17 DOI: 10.1109/LSP.2025.3542965
Samrat Mukhopadhyay;Debasmita Mukherjee
{"title":"Follow the Approximate Sparse Leader for No-Regret Online Sparse Linear Approximation","authors":"Samrat Mukhopadhyay;Debasmita Mukherjee","doi":"10.1109/LSP.2025.3542965","DOIUrl":"https://doi.org/10.1109/LSP.2025.3542965","url":null,"abstract":"We consider the problem of <italic>online sparse linear approximation</i>, where a learner sequentially predicts the best sparse linear approximations of an as yet unobserved sequence of measurements in terms of a few columns of a given measurement matrix. The inherent difficulty of offline sparse recovery makes the online problem challenging as well. In this letter, we propose Follow-The-Approximate-Sparse-Leader, an efficient online meta-policy to address this online problem. Through a detailed theoretical analysis, we prove that under certain assumptions on the measurement sequence, the proposed policy enjoys a data-dependent sublinear upper bound on the static regret, which can range from logarithmic to square-root. Extensive numerical simulations are performed to corroborate the theoretical findings and demonstrate the efficacy of the proposed online policy.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"951-955"},"PeriodicalIF":3.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553432","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
Untraceable Steganography: Towards the Anonymity of Steganographer
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-17 DOI: 10.1109/LSP.2025.3543103
Zichi Wang;Xinpeng Zhang;Xinran Li
{"title":"Untraceable Steganography: Towards the Anonymity of Steganographer","authors":"Zichi Wang;Xinpeng Zhang;Xinran Li","doi":"10.1109/LSP.2025.3543103","DOIUrl":"https://doi.org/10.1109/LSP.2025.3543103","url":null,"abstract":"To protect sender's identity of covert communication, this paper proposes a new concept of steganography called untraceable steganography. The receiver can extract secret data from stego media without knowing who was the sender. Specifically, the stego media is produced by a number of individuals (including the sender and other normal individuals). Thus, the receiver cannot know who was the sender, since the media produced by the sender is only a part of stego media and the receiver cannot find the part produced by the sender from stego media. Therefore, the sender is anonymous during the whole process of steganography. That means this kind of steganography is untraceable. In this case, the sender's identity can be protected, and thus the security of steganography can be advanced in a higher level. A specific untraceable steganography scheme is designed in this paper for digital images, which achieves the function of untraceable steganography without decrease the undetectability of stego media.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"956-960"},"PeriodicalIF":3.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553433","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
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