IEEE open journal of signal processing最新文献_第2页

Cross-Dataset Head-Related Transfer Function Harmonization Based on Perceptually Relevant Loss Function 基于感知相关损失函数的跨数据集头部相关传递函数协调

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-07-17 DOI: 10.1109/OJSP.2025.3590248

Jiale Zhao;Dingding Yao;Junfeng Li

{"title":"Cross-Dataset Head-Related Transfer Function Harmonization Based on Perceptually Relevant Loss Function","authors":"Jiale Zhao;Dingding Yao;Junfeng Li","doi":"10.1109/OJSP.2025.3590248","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3590248","url":null,"abstract":"Head-Related Transfer Functions (HRTFs) play a vital role in binaural spatial audio rendering. With the release of numerous HRTF datasets in recent years, abundant data has become available to support HRTF-related research based on deep learning. However, measurement discrepancies across different datasets introduce significant variations in the data and directly merging these datasets may lead to systematic biases. The recent Listener Acoustic Personalization Challenge 2024 (European Signal Processing Conference) dealt with this issue, with the task of harmonizing different datasets to achieve lower classification accuracy while meeting thresholds over various localization metrics. To mitigate cross-dataset differences, this paper proposes a neural network-based HRTF harmonization approach aimed at eliminating dataset-specific properties embedded in the original measurements. The proposed method utilizes a perceptually relevant loss function, which jointly constrains multiple objectives, including interaural level differences, auditory-filter excitation patterns, and classification accuracy. Experimental results based on eight datasets demonstrate that the proposed approach can effectively minimize distributional disparities between datasets while mostly preserving localization performance. The classification accuracy for harmonized HRTFs between different datasets is reduced to as low as 31%, indicating a significant reduction in cross-dataset discrepancies. The proposed method ranked first in this challenge, which validates its effectiveness.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"865-875"},"PeriodicalIF":2.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739816","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}

引用次数: 0

Tell Me What You See: Text-Guided Real-World Image Denoising 告诉我你看到了什么：文本引导的真实世界图像去噪

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-07-14 DOI: 10.1109/OJSP.2025.3588715

Erez Yosef;Raja Giryes

引用次数: 0

Learning Graph Structures With Autoregressive Graph Signal Models 用自回归图信号模型学习图结构

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-07-11 DOI: 10.1109/OJSP.2025.3588447

Kyle Donoghue;Ashkan Ashrafi

{"title":"Learning Graph Structures With Autoregressive Graph Signal Models","authors":"Kyle Donoghue;Ashkan Ashrafi","doi":"10.1109/OJSP.2025.3588447","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3588447","url":null,"abstract":"This paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals with propagation delay. GL-AR can discern graph structures where propagation between vertices is delayed, mirroring the dynamics of many real-world systems. This is achieved by utilizing the autoregressive coefficients of time-series graph signals in GL-AR’s learning algorithm. Existing graph learning techniques typically minimize the smoothness of a graph signal on a recovered graph structure to learn instantaneous relationships. GL-AR extends this approach by showing that minimizing smoothness with autoregressive coefficients can additionally recover relationships with propagation delay. The efficacy of GL-AR is demonstrated through applications to both synthetic and real-world datasets. Specifically, this work introduces the Graph-Tensor Method, a novel technique for generating synthetic time-series graph signals that represent edges as transfer functions. This method, along with real-world data from the National Climatic Data Center, is used to evaluate GL-AR’s performance in recovering undirected graph structures. Results indicate that GL-AR’s use of autoregressive coefficients enables it to outperform state-of-the-art graph learning techniques in scenarios with nonzero propagation delays. Furthermore, GL-AR’s performance is optimized by a new automated parameter selection algorithm, which eliminates the need for computationally intensive trial-and-error methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"838-855"},"PeriodicalIF":2.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11078159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725118","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}

引用次数: 0

A Factor Graph Approach to Variational Sparse Gaussian Processes 变分稀疏高斯过程的因子图方法

IF 2.9

IEEE open journal of signal processing Pub Date : 2025-07-02 DOI: 10.1109/OJSP.2025.3585440

Hoang Minh Huu Nguyen;İsmaıl Şenöz;Bert De Vries

{"title":"A Factor Graph Approach to Variational Sparse Gaussian Processes","authors":"Hoang Minh Huu Nguyen;İsmaıl Şenöz;Bert De Vries","doi":"10.1109/OJSP.2025.3585440","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3585440","url":null,"abstract":"A Variational Sparse Gaussian Process (VSGP) is a sophisticated nonparametric probabilistic model that has gained significant popularity since its inception. The VSGP model is often employed as a component of larger models or in a modified form across numerous applications. However, re-deriving the update equations for inference in these variations is technically challenging, which hinders broader adoption. In a separate line of research, message passing-based inference in factor graphs has emerged as an efficient framework for automated Bayesian inference. Despite its advantages, message passing techniques have not yet been applied to VSGP-based models due to the lack of a suitable representation for VSGP models in factor graphs. To address this limitation, we introduce a Sparse Gaussian Process (SGP) node within a Forney-style factor graph (FFG). We derive variational message passing update rules for the SGP node, enabling automated and efficient inference for VSGP-based models. We validate the update rules and illustrate the benefits of the SGP node through experiments in various Gaussian Process applications.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"815-837"},"PeriodicalIF":2.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11063321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680885","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}

引用次数: 0

Model Predictive Control Algorithm for Video Coding and Uplink Delivery in Delay-Critical Applications 延迟关键应用中视频编码和上行传输的模型预测控制算法

IF 2.7

IEEE open journal of signal processing Pub Date : 2025-06-30 DOI: 10.1109/OJSP.2025.3584672

Mourad Aklouf;Frédéric Dufaux;Michel Kieffer;Marc Lény

{"title":"Model Predictive Control Algorithm for Video Coding and Uplink Delivery in Delay-Critical Applications","authors":"Mourad Aklouf;Frédéric Dufaux;Michel Kieffer;Marc Lény","doi":"10.1109/OJSP.2025.3584672","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3584672","url":null,"abstract":"Emerging applications such as remote car driving, drone control, or distant mobile robot operation impose a very tight constraint on the delay between the acquisition of a video frame by a camera embedded in the operated device and its display at the remote controller. This paper introduces a new frame-level video encoder rate control technique for ultra-low-latency video coding and delivery. A Model Predictive Control approach, exploiting the buffer level at the transmitter and an estimate of the transmission rate, is used to determine the target encoding rate of each video frame to adapt with minimum delay to sudden variations of the transmission channel characteristics. Then, an <inline-formula><tex-math>$R-(QP,D)$</tex-math></inline-formula> model of the rate <inline-formula><tex-math>$R$</tex-math></inline-formula> of the current frame to be encoded as a function of its quantization parameter (QP) and of the distortion <inline-formula><tex-math>$D$</tex-math></inline-formula> of the reference frame is used to get the QP matching the target rate. This QP is then fed to the video coder. The proposed approach is compared to reference algorithms, namely PANDA, FESTIVE, BBA, and BOLA, some of which have been adapted to the considered server-driven low-latency coding and transmission scenario. Simulation results based on 4G bandwidth traces show that the proposed algorithm outperforms the others at different glass-to-glass delay constraints, considering several video quality metrics.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"876-889"},"PeriodicalIF":2.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11059858","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750801","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}

引用次数: 0

Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed Images 利用冷扩散对同分布叠加图像进行分解

IF 2.9

IEEE open journal of signal processing Pub Date : 2025-06-27 DOI: 10.1109/OJSP.2025.3583963

Helena Montenegro;Jaime S. Cardoso

{"title":"Leveraging Cold Diffusion for the Decomposition of Identically Distributed Superimposed Images","authors":"Helena Montenegro;Jaime S. Cardoso","doi":"10.1109/OJSP.2025.3583963","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3583963","url":null,"abstract":"With the growing adoption of Deep Learning for imaging tasks in biometrics and healthcare, it becomes increasingly important to ensure privacy when using and sharing images of people. Several works enable privacy-preserving image sharing by anonymizing the images so that the corresponding individuals are no longer recognizable. Most works average images or their embeddings as an anonymization technique, relying on the assumption that the average operation is irreversible. Recently, cold diffusion models, based on the popular denoising diffusion probabilistic models, have succeeded in reversing deterministic transformations on images. In this work, we leverage cold diffusion to decompose superimposed images, empirically demonstrating that it is possible to obtain two or more identically-distributed images given their average. We propose novel sampling strategies for this task and show their efficacy on three datasets. Our findings highlight the risks of averaging images as an anonymization technique and argue for the use of alternative anonymization strategies.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"784-794"},"PeriodicalIF":2.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11054277","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606192","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}

引用次数: 0

Tiny-VPS: Tiny Video Panoptic Segmentation Standing on the Shoulder of Giant-VPS Tiny- vps：站在Giant-VPS肩膀上的微型视频全景分割

IF 2.9

IEEE open journal of signal processing Pub Date : 2025-06-20 DOI: 10.1109/OJSP.2025.3581840

Qingfeng Liu;Mostafa El-Khamy;Kee-Bong Song

引用次数: 0

Biorthogonal Lattice Tunable Wavelet Units and Their Implementation in Convolutional Neural Networks for Computer Vision Problems 双正交格子可调小波单元及其在卷积神经网络中的实现

IF 2.9

IEEE open journal of signal processing Pub Date : 2025-06-18 DOI: 10.1109/OJSP.2025.3580967

An D. Le;Shiwei Jin;Sungbal Seo;You-Suk Bae;Truong Q. Nguyen

引用次数: 0

In-Scene Calibration of Poisson Noise Parameters for Phase Image Recovery 相位图像恢复泊松噪声参数的现场标定

IF 2.9

IEEE open journal of signal processing Pub Date : 2025-06-13 DOI: 10.1109/OJSP.2025.3579650

Achour Idoughi;Sreelakshmi Sreeharan;Chen Zhang;Joseph Raffoul;Hui Wang;Keigo Hirakawa

{"title":"In-Scene Calibration of Poisson Noise Parameters for Phase Image Recovery","authors":"Achour Idoughi;Sreelakshmi Sreeharan;Chen Zhang;Joseph Raffoul;Hui Wang;Keigo Hirakawa","doi":"10.1109/OJSP.2025.3579650","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3579650","url":null,"abstract":"In sensor metrology, noise parameters governing the stochastic nature of photon detectors play critical role in characterizing the aleatoric uncertainty of computational imaging systems such as indirect time-of-flight cameras, structured light imaging, and division-of-time polarimetric imaging. Standard calibration procedures exists for extracting the noise parameters using calibration targets, but they are inconvenient or impractical for frequent updates. To keep up with noise parameters that are dynamically affected by sensor settings (e.g. exposure and gain) as well as environmental factors (e.g. temperature), we propose an In-Scene Calibration of Poisson Noise Parameters (ISC-PNP) method that does not require calibration targets. The main challenge lies in the heteroskedastic nature of the noise and the confounding influence of scene content. To address this, our method leverages global joint statistics of Poisson sensor data, which can be interpreted as a binomial random variable. We experimentally confirm that the noise parameters extracted by the proposed ISC-PNP and the standard calibration procedure are well-matched.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"682-690"},"PeriodicalIF":2.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11034763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511201","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}

引用次数: 0

Continuous Relaxation of Discontinuous Shrinkage Operator: Proximal Inclusion and Conversion 不连续收缩算子的连续松弛：近端包合与转换

IF 2.9

IEEE open journal of signal processing Pub Date : 2025-06-13 DOI: 10.1109/OJSP.2025.3579646

Masahiro Yukawa

引用次数: 0