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IR-Pro: Baking Probes to Model Indirect Illumination for Inverse Rendering of Scenes
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-24 DOI: 10.1109/LSP.2025.3545291
Zhihao Liang;Qi Zhang;Yirui Guan;Ying Feng;Kui Jia
{"title":"IR-Pro: Baking Probes to Model Indirect Illumination for Inverse Rendering of Scenes","authors":"Zhihao Liang;Qi Zhang;Yirui Guan;Ying Feng;Kui Jia","doi":"10.1109/LSP.2025.3545291","DOIUrl":"https://doi.org/10.1109/LSP.2025.3545291","url":null,"abstract":"Modeling indirect illumination to handle global illumination and decompose materials from multi-view images is challenging, especially in complex scenes with self-occlusion. While recent implicit neural representations show promise in inverse rendering, they struggle with efficient and effective modeling of indirect illumination. Besides, real-time global illumination techniques (e.g. Indirect Lighting Cache) have been successful in gaming. Inspired by this, we present a novel three-stage <bold>I</b>nverse <bold>R</b>enderer with <bold>Pro</b>bes (IR-Pro), which efficiently caches occlusion to handle indirect illumination. Experiments demonstrate the superiority of IR-Pro over existing methods in the inverse rendering of complex scenes. Furthermore, we successfully integrate the results into digital content creation software and showcase their effectiveness in applications, like relighting, simulation, and editing.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1126-1130"},"PeriodicalIF":3.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654933","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
Paradoxical Role of Adversarial Attacks: Enabling Crosslinguistic Attacks and Information Hiding in Multilingual Speech Recognition
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-24 DOI: 10.1109/LSP.2025.3545276
Wenjie Zhang;Zhihua Xia;Bin Ma;Diqun Yan
{"title":"Paradoxical Role of Adversarial Attacks: Enabling Crosslinguistic Attacks and Information Hiding in Multilingual Speech Recognition","authors":"Wenjie Zhang;Zhihua Xia;Bin Ma;Diqun Yan","doi":"10.1109/LSP.2025.3545276","DOIUrl":"https://doi.org/10.1109/LSP.2025.3545276","url":null,"abstract":"With the rise of automatic speech recognition (ASR) research and practical applications, enabling adversarial attacks on ASR systems via subtle perturbations has become a priority. Most prior research has focused on single-language, single-model ASR systems. However, multilingual ASR systems hold opportunities for crosslinguistic attacks and covert message transmission. This letter introduces a new approach for crosslinguistic adversarial attacks in multilingual ASR, focusing on information hiding. For example, in military settings, adversarial examples applied to eavesdropping devices can encode messages detectable only by friendly devices, leaving adversaries, even with identical methods, unable to access them. This letter examines multilingual ASR system properties and introduces a crosslinguistic adversarial example with minimal perturbation, allowing friendly classifiers to extract hidden information while being undetectable by hostile classifiers. The experimental results on 5 models and 5 datasets show that the proposed method achieves a success rate of over 90% and an SNR close to 40 dB.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1046-1050"},"PeriodicalIF":3.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594315","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 Low-Complexity Sparse Bayesian Learning With Embedded Bayesian Threshold
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-21 DOI: 10.1109/LSP.2025.3544536
Yifei Yang;Tengfei Qi;Qianli Wang;Pengcheng Zhu;Xiong Deng
{"title":"Improved Low-Complexity Sparse Bayesian Learning With Embedded Bayesian Threshold","authors":"Yifei Yang;Tengfei Qi;Qianli Wang;Pengcheng Zhu;Xiong Deng","doi":"10.1109/LSP.2025.3544536","DOIUrl":"https://doi.org/10.1109/LSP.2025.3544536","url":null,"abstract":"Sparse Bayesian Learning (SBL) is recognized for its efficacy in sparse signal recovery, the computational demand escalates significantly with increasing data dimensionality due to the matrix inversion at each iteration. An Inverse-Free sparse Bayesian Learning (IF-SBL) approach has been introduced to mitigate computational complexity. However, IF-SBL converges easily to a sub-optimal solution with false peaks due to the neglect of the correlation between atoms. In this paper, we analyze causes of false peaks in IF-SBL. Subsequently, a novel dynamically updated embedded Bayesian threshold is designed to mitigate the interference caused by false peaks. This innovative approach retrieves the stability and reliability without significantly increasing signal recovery complexity compared with IF-SBL. Simulation experiments validate the results.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1066-1070"},"PeriodicalIF":3.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629592","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
Outlier-Resilient Model Fitting via Percentile Losses: Methods for General and Convex Residuals
IF 3.2 2区 工程技术
IEEE Signal Processing Letters Pub Date : 2025-02-20 DOI: 10.1109/LSP.2025.3542330
João Domingos;João Xavier
{"title":"Outlier-Resilient Model Fitting via Percentile Losses: Methods for General and Convex Residuals","authors":"João Domingos;João Xavier","doi":"10.1109/LSP.2025.3542330","DOIUrl":"https://doi.org/10.1109/LSP.2025.3542330","url":null,"abstract":"We consider the problem of robustly fitting a model to data that includes outliers by formulating a percentile optimization problem. This problem is non-smooth and non-convex, hence hard to solve. We derive properties that the minimizers of such problems must satisfy. These properties lead to methods that solve the percentile formulation both for general residuals and for convex residuals. The methods fit the model to subsets of the data, and then extract the solution of the percentile formulation from these partial fits. As illustrative simulations show, such methods endure higher outlier percentages, when compared with standard robust estimates. Additionally, the derived properties provide a broader and alternative theoretical validation for existing robust methods, whose validity was previously limited to specific forms of the residuals.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"931-935"},"PeriodicalIF":3.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553415","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
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
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