IEEE open journal of signal processing最新文献

{"title":"BGM2Pose: Active 3D Human Pose Estimation With Non-Stationary Sounds","authors":"Yuto Shibata;Yusuke Oumi;Go Irie;Akisato Kimura;Yoshimitsu Aoki;Mariko Isogawa","doi":"10.1109/OJSP.2026.3705347","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3705347","url":null,"abstract":"We propose BGM2Pose, a non-invasive 3D human pose estimation method using pre-selected everyday music (e.g., background music) as active sensing signals. Unlike existing approaches that significantly limit practicality by employing intrusive chirp signals within the audible range, our method utilizes user-selected natural music that causes minimal discomfort to listeners. Estimating human poses from standard music presents significant challenges. In contrast to sound sources specifically designed for measurement, regular music varies in both volume and pitch. These dynamic changes in signals caused by music are inevitably mixed with alterations in the sound field resulting from human motion, making it hard to extract reliable cues for pose estimation. To address these challenges, BGM2Pose introduces a Contrastive Pose Extraction Module that employs contrastive learning and hard negative sampling to eliminate musical components from the recorded data, isolating the pose information. Additionally, we propose a Frequency-wise Attention Module that enables the model to focus on subtle acoustic variations attributable to human movement by dynamically computing attention across frequency bands. Experiments suggest that our method outperforms the existing methods, demonstrating substantial potential for real-world applications. Our datasets and code will be made publicly available.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"790-799"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11570037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameter Efficient Adaptation of Vision Large Language Models for Fisheye Object Detection","authors":"Amine Bechar;Abbes Amira;Fatih Kurugollu;Adel Oulefki;Munkhjargal Gochoo;Fady Alnajjar","doi":"10.1109/OJSP.2026.3706022","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3706022","url":null,"abstract":"Fisheye cameras are used for coverage in transportation systems and vehicles, yet distortions pose challenges to object detection algorithms. In this paper, Radial-Aware LoRA (RA-LoRA) is proposed, a distortion-aware parameter-efficient adaptation method that modulates low-rank updates based on radial position to address the spatially varying nature of fisheye distortion. RA-LoRA fine-tuning of Florence-2-large with only 0.5% additional parameters achieves a 181% relative improvement in mAP@50 over zero-shot inference, outperforming fully trained CNN detectors in fisheye imagery. Detection performance is assessed in all five object classes of the FishEye8K dataset (bike, bus, car, pedestrian, and truck). The results show that Florence-2-large with RA-LoRA achieves a mean average precision (mAP) of 0.724, outperforming YOLO26x (mAP: 0.581) and YOLO26 l (mAP: 0.605). Although the model requires approximately 4.5× more memory than YOLO26, the RA-LoRA adapters represent only 0.5% of the total parameters and can be merged into the base model at inference time with no additional overhead. This makes the proposed approach particularly suitable for server-side processing, offline traffic analysis, post-incident auditing, and high-accuracy surveillance applications where detection reliability outweighs the need for real-time embedded deployment.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"746-752"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11573650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gridless Variational Bayesian DoA Estimation for Few-Bit Antenna Arrays","authors":"Toan-Van Nguyen;A. Lee Swindlehurst;Duy H. N. Nguyen","doi":"10.1109/OJSP.2026.3710112","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3710112","url":null,"abstract":"Efficient and accurate direction of arrival (DoA) estimation is important in radar, wireless communication, and integrated sensing and communication systems. We propose a novel gridless DoA estimation algorithm within the variational Bayesian (VB) framework, tailored for antenna arrays equipped with few-bit analog-to-digital converters. The proposed approach facilitates the derivation of variational distributions for the spatial frequencies of the incoming paths and their associated path gains. Simulation results reveal that the proposed gridless-VB algorithm achieves superior normalized mean-squared error performance for DoA estimation compared to MUSIC, variational line spectral estimation (VALSE), and VALSE expectation propagation (VALSE-EP) algorithms, while maintaining comparable computational complexity. Additionally, the gridless-VB algorithm achieves the best MSE among the considered algorithms when evaluating the angle, gain, and phase estimation under different ranges of SNRs and bit depths. The impacts of key parameters such as the number of antennas, the number of paths, and the bit-depth of the quantizers are also comprehensively evaluated, offering valuable insights for algorithm design and optimization.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"776-789"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11594924","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SMNet: Spatial Information-Aware Multi-Level Fusion Network for Medical Image Segmentation","authors":"Chengming Huang","doi":"10.1109/OJSP.2026.3680782","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3680782","url":null,"abstract":"This paper discusses the limitations of U-Net structure-based medical image segmentation methods and the employment of traditional attention mechanisms (channel or spatial attention). To this end, we propose a novel spatial information-aware multi-level fusion network tailored for medical image segmentation, which incorporates the multi-level deep fusion (MDF) module, channel-spatial semantic interaction (CSSI) module and split detail enhancement (SDE) module. These modules are designed to harmonize feature integration across scales, unearth positional cues from high-level features by exploring their intrinsic channel and spatial relationships, and enhance fine details via variable dilation rates in low-level features, respectively. Additionally, cross-level connections are established to counter the information loss typically associated with bottom-up feature transmission, ensuring a more comprehensive fusion of location and detail information. We conduct extensive experiments on the three medical image datasets, and comparison results show that the proposed SMNet has superior performance.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"428-437"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11474926","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Multichannel Wiener Filtering for Topology-Unconstrained Wireless Acoustic Sensor Networks","authors":"Paul Didier;Pourya Behmandpoor;Henri Gode;Toon van Waterschoot;Simon Doclo;Jörg Bitzer;Marc Moonen","doi":"10.1109/OJSP.2026.3721064","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3721064","url":null,"abstract":"This paper introduces the topology-independent distributed multichannel Wiener filter (TI-dMWF), a novel algorithm for distributed node-specific signal estimation in wireless acoustic sensor networks (WASNs) with unconstrained topologies. The TI-dMWF enables each node in the network to compute its centralized multichannel Wiener filter solution by exchanging only low-dimensional fused signals, without requiring iterative estimation, unlike state-of-the-art approaches such as the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm. The TI-dMWF is proven optimal when each source is observed by either all nodes or only one node. Theoretical analysis and numerical simulations confirm that it achieves centralized estimation performance in a single run. Its latency as a function of the pruned-tree depth and its computational complexity are also analyzed. Its robustness is assessed in reverberant-room simulations under estimated second-order statistics, various network topologies, and deviations from the assumed observability model.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"866-873"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11643568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Acoustic Mixture-of-Experts for Spatial Sound Estimation","authors":"Gioele Greco;Dongzhe Zhang;Mirco Pezzoli;Fabio Antonacci","doi":"10.1109/OJSP.2026.3693608","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3693608","url":null,"abstract":"Accurate spatial acoustic characterization is crucial for immersive audio applications, such as virtual and augmented reality, which require low-latency, high-fidelity rendering of sound fields. Traditional approaches, including physics-based simulations and centralized deep learning models, face significant challenges in scalability, data efficiency, and robustness under sparse measurement conditions, and typically require retraining or fine-tuning whenever new measurements are acquired. This paper introduces DAME, a Distributed Acoustic Mixture-of-Experts framework for large-area spatial acoustic modeling. DAME decomposes the global sound field reconstruction task into localized sub-problems, each handled by a compact neural expert trained on region-specific data, while a lightweight gating network combines the experts' predictions based on spatial queries. Unlike state-of-the-art methods, the framework supports incremental expansion on the receiver side. Specifically, new experts can be added to cover previously unsampled regions, and only the gating network is updated, avoiding full retraining of the model and preserving previously learned behavior. Evaluated on both simulated and real-world higher-order Ambisonics room impulse responses, DAME consistently outperforms state-of-the-art parametric, kernel-based, and physics-informed baselines in terms of reconstruction accuracy, directional estimation, and energy decay preservation. Computational analysis demonstrates its suitability for low-latency operation, and a perceptual evaluation confirms superior audio quality and spatial fidelity. The framework therefore provides a scalable, data-efficient alternative for robust spatial audio reconstruction in challenging sparse-data regimes.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"569-580"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Riemannian Covariance Enhancement for Robust Direct Position Determination Under Intermittent Interference","authors":"Yehonatan-Itay Segman;Joseph S. Picard;Ronen Talmon","doi":"10.1109/OJSP.2026.3733589","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3733589","url":null,"abstract":"Direct position determination (DPD) localizes a source directly from received array data, but its performance can degrade under model mismatch. In this work, we consider DPD in the presence of intermittent interfering sources, which lead to covariance contamination and spurious peaks in the DPD heatmap. To address this, we propose a covariance-enhancement approach based on the Riemannian geometry of the manifold of Hermitian positive definite matrices. The proposed enhancement procedure is built around the Riemannian mean of covariance matrices, and the resulting enhanced covariance is then used to compute the standard DPD heatmaps. We provide theoretical support, in several settings, showing that the Riemannian mean serves as a mechanism for attenuating intermittent interference. Numerical simulations demonstrate improved localization accuracy in challenging scenarios, including multiple desired transmitters and multiple intermittent interfering sources.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"972-988"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11689742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Block Double Proportionate Sign Adaptive Filter: Derivation and Convergence Analysis","authors":"Mohammad Salman;Hadi Zayyani;Felipe A. P. de Figueiredo;Hasan Abu Hilal;Mostafa Rashdan","doi":"10.1109/OJSP.2026.3734824","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3734824","url":null,"abstract":"This paper proposes the Block Double Proportionate Sign Adaptive Filter (BDP-SAF) for sparse system identification in impulsive noise environments. The algorithm brings three mechanisms that are individually established in the adaptive filtering literature, namely block-level proportionate adaptation, zero-attraction and the sign-error update, and combines them with a single block-level method. The algorithm is derived from a regularized cost function combining <inline-formula><tex-math>$ell _{2}$</tex-math></inline-formula> and block-level <inline-formula><tex-math>$ell _{1}$</tex-math></inline-formula> penalty terms, which yields stationary-point expressions for a block proportionate gain matrix and a block zero-attractor; these are then reduced using approximations to a pair of low-cost implementable gains. The resulting update rule simultaneously accelerates adaptation in active blocks and suppresses inactive ones through a unified block energy estimate, while the sign-error update improves robustness to heavy-tailed disturbances under only a symmetry assumption on the noise distribution, without requiring knowledge of its higher-order moments or parametric form. To handle practical scenarios where the block partition is unavailable or misaligned with the true impulse response support, an adaptive block boundary detection scheme is developed that progressively realigns the partition online with negligible computational overhead. A convergence analysis establishes sufficient conditions for mean stability, and a semi-analytical steady-state mean-square deviation (MSD) expression is derived, together with an explicit statement of the assumptions on which both results rely. Simulation results are consistent with the theoretical findings and demonstrate that BDP-SAF achieves lower steady-state MSD with or without abrupt channel changes compared to the considered competing algorithms, including the case of several simultaneously active blocks.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"1006-1020"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11694241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Probabilistic Generative Model for Spectral Speech Enhancement","authors":"Marco Hidalgo-Araya;Raphaël Trésor;Bart Van Erp;Wouter W.L. Nuijten;Thijs Van De Laar;Bert De Vries","doi":"10.1109/OJSP.2026.3695788","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3695788","url":null,"abstract":"Speech enhancement in hearing aids remains difficult in nonstationary acoustic environments. This paper introduces AIDA-2, a unified modular framework that formulates signal processing, learning, and personalization as Bayesian inference with explicit uncertainty tracking. The proposed framework replaces ad hoc algorithm design with a single probabilistic generative model that continuously adapts to changing acoustic conditions and user preferences. It extends spectral subtraction with principled mechanisms for in situ personalization and adaptation to acoustic context. The system is implemented as an interconnected probabilistic state-space model, and inference is performed via variational message passing in the <monospace>RxInfer.jl</monospace> probabilistic programming environment, enabling real-time Bayesian processing under hearing-aid constraints. We focus the evaluation on the warped-frequency filter bank (WFB) and speech enhancement model (SEM), evaluated on the public <italic>VoiceBank+DEMAND</i> corpus. Additionally, an acoustic context model (ACM) and end-user model (EUM) are described architecturally. The evaluated system requires 85 parameters, two orders of magnitude fewer than the tens of thousands to millions of trainable parameters used by contemporary deep-learning baselines. This proof of concept establishes an interpretable, data-efficient foundation for uncertainty-aware hearing-aid processing, pointing toward devices that learn continuously through probabilistic inference.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"591-611"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11532965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Time Warping for Secondary Path Interpolation in Local Active Noise Control","authors":"Felix Holzmüller;Alois Sontacchi","doi":"10.1109/OJSP.2026.3689448","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3689448","url":null,"abstract":"For stable and performant local active noise control (ANC), accurate estimates of the paths between secondary sources and the desired points of cancellation are required. When these paths are recorded prior to operation for a limited number of positions, this poses a problem in scenes with moving points of cancellation. In this article, we propose an interpolation approach for filter coefficients of secondary paths. By applying dynamic time warping (DTW) in an offline analysis, impulse responses are properly aligned before interpolation and de-warping during operation. The system is benchmarked against nearest-neighbor and linear interpolation with and without global time alignment for translation and rotation of a listener’s head. Our DTW-based technique exhibits lower overall system mismatch and extends the frequency range for stable operation, especially for lateral translation and coarse measurement grids. In simulations, higher noise reduction and extended frequency range of stable operation could be observed compared to the other baseline systems. The proposed technique can reduce the required number of measurement positions of secondary paths substantially while increasing the controlled frequency bandwidth for timevariant realworld systems.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"492-500"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11501723","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}