IEEE open journal of signal processing最新文献_第10页

Unrolling of Simplicial ElasticNet for Edge Flow Signal Reconstruction 用于边缘流信号重构的简化弹性网络的展开

IEEE open journal of signal processing Pub Date : 2023-12-05 DOI: 10.1109/OJSP.2023.3339376

Chengen Liu;Geert Leus;Elvin Isufi

{"title":"Unrolling of Simplicial ElasticNet for Edge Flow Signal Reconstruction","authors":"Chengen Liu;Geert Leus;Elvin Isufi","doi":"10.1109/OJSP.2023.3339376","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3339376","url":null,"abstract":"The edge flow reconstruction task consists of retreiving edge flow signals from corrupted or incomplete measurements. This is typically solved by a regularized optimization problem on higher-order networks such as simplicial complexes and the corresponding regularizers are chosen based on prior knowledge. Tailoring this prior to the setting of interest can be challenging or it may not even be possible. Thus, we consider to learn this prior knowledge via a model-based deep learning approach. We propose a new regularized optimization problem for the simplicial edge flow reconstruction task, the simplicial ElasticNet, which combines the advantages of the \u0000<inline-formula><tex-math>$ell _{1}$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$ell _{2}$</tex-math></inline-formula>\u0000 norms. We solve the simplicial ElasticNet problem via the multi-block alternating direction method of multipliers (ADMM) algorithm and provide conditions on its convergence. By unrolling the ADMM iterative steps, we develop a model-based neural network with a low requirement on the number of training data. This unrolling network replaces the fixed parameters in the iterative algorithm by learnable weights, thus exploiting the neural network's learning capability while preserving the iterative algorithm's interpretability. We enhance this unrolling network via simplicial convolutional filters to aggregate information from the edge flow neighbors, ultimately, improving the network learning expressivity. Extensive experiments on real-world and synthetic datasets validate the proposed approaches and show considerable improvements over both baselines and traditional non-model-based neural networks.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"186-194"},"PeriodicalIF":0.0,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10342735","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060255","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 Generalized Bandsplit Neural Network for Cinematic Audio Source Separation 用于电影音源分离的广义分带神经网络

IEEE open journal of signal processing Pub Date : 2023-12-05 DOI: 10.1109/OJSP.2023.3339428

Karn N. Watcharasupat;Chih-Wei Wu;Yiwei Ding;Iroro Orife;Aaron J. Hipple;Phillip A. Williams;Scott Kramer;Alexander Lerch;William Wolcott

{"title":"A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation","authors":"Karn N. Watcharasupat;Chih-Wei Wu;Yiwei Ding;Iroro Orife;Aaron J. Hipple;Phillip A. Williams;Scott Kramer;Alexander Lerch;William Wolcott","doi":"10.1109/OJSP.2023.3339428","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3339428","url":null,"abstract":"Cinematic audio source separation is a relatively new subtask of audio source separation, with the aim of extracting the dialogue, music, and effects stems from their mixture. In this work, we developed a model generalizing the Bandsplit RNN for any complete or overcomplete partitions of the frequency axis. Psychoacoustically motivated frequency scales were used to inform the band definitions which are now defined with redundancy for more reliable feature extraction. A loss function motivated by the signal-to-noise ratio and the sparsity-promoting property of the 1-norm was proposed. We additionally exploit the information-sharing property of a common-encoder setup to reduce computational complexity during both training and inference, improve separation performance for hard-to-generalize classes of sounds, and allow flexibility during inference time with detachable decoders. Our best model sets the state of the art on the Divide and Remaster dataset with performance above the ideal ratio mask for the dialogue stem.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"73-81"},"PeriodicalIF":0.0,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10342812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060182","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 Neural-Enhanced Factor Graph-Based Algorithm for Robust Positioning in Obstructed LOS Situations 基于神经增强因子图的算法，用于在有障碍的 LOS 情况下进行稳健定位

IEEE open journal of signal processing Pub Date : 2023-11-30 DOI: 10.1109/OJSP.2023.3338113

Alexander Venus;Erik Leitinger;Stefan Tertinek;Klaus Witrisal

{"title":"A Neural-Enhanced Factor Graph-Based Algorithm for Robust Positioning in Obstructed LOS Situations","authors":"Alexander Venus;Erik Leitinger;Stefan Tertinek;Klaus Witrisal","doi":"10.1109/OJSP.2023.3338113","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3338113","url":null,"abstract":"This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models. The proposed algorithm uses radio signal measurements from multiple base stations to robustly estimate the mobile agent's position together with all model parameters. It provides high localization accuracy by exploiting the position-related information of the LOS component via the physics-based model and robustness by exploiting the geometric imprint of multipath components independent of the propagation channel via the data-driven model. In a challenging numerical experiment involving obstructed LOS situations to all anchors, we show that the proposed sequential algorithm significantly outperforms state-of-the-art methods and attains the posterior Cramér-Rao lower bound even with training data limited to local regions.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"29-38"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10336409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060334","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

Moving Target At Constant Velocity Localization Using TOA Measurements From Single Moving Receiver With Unknown Signal Period 利用信号周期未知的单个移动接收器的 TOA 测量进行匀速运动目标定位

IEEE open journal of signal processing Pub Date : 2023-11-30 DOI: 10.1109/OJSP.2023.3338111

Yanbin Zou;Jingna Fan;Zekai Zhang

{"title":"Moving Target At Constant Velocity Localization Using TOA Measurements From Single Moving Receiver With Unknown Signal Period","authors":"Yanbin Zou;Jingna Fan;Zekai Zhang","doi":"10.1109/OJSP.2023.3338111","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3338111","url":null,"abstract":"In this article, we consider using time-of-arrival (TOA) measurements from a single moving receiver to locate a moving target at constant velocity that emits a periodic signal with unknown signal period. First, we give the TOA measurement model and deduce the Cram \u0000<inline-formula><tex-math>$acute{text{e}}$</tex-math></inline-formula>\u0000 r-Rao lower bounds (CRLB). Then, we formulate a nonlinear least squares (NLS) problem to estimate the unknown parameters. We use semidefinite programming (SDP) techniques to relax the nonconvex NLS problem. However, it is shown that the SDP localization algorithm cannot provide a high-quality solution. Subsequently, we develop a fixed point iteration (FPI) method to improve the performance of the SDP algorithm. In addition, we also consider the presence of receiver position errors and develop the corresponding localization algorithm. Numerical simulations are conducted to demonstrate the localization performance of the proposed algorithms by comparing them with the CRLB. \u0000<italic>Index Term</i>\u0000-Fixed point iteration (FPI), semidefinite programming (SDP), single moving receiver, target localization, time-of-arrival (TOA).","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"10-18"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10336384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060184","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

Analytical Performance Assessment of 2-D Tensor ESPRIT in Terms of Physical Parameters 根据物理参数对二维张量 ESPRIT 进行分析性能评估

IEEE open journal of signal processing Pub Date : 2023-11-29 DOI: 10.1109/OJSP.2023.3337729

Damir Rakhimov;Martin Haardt

引用次数: 0

A Flexible Framework for Expectation Maximization-Based MIMO System Identification for Time-Variant Linear Acoustic Systems 时变线性声学系统基于期望最大化的多输入多输出系统识别的灵活框架

IEEE open journal of signal processing Pub Date : 2023-11-29 DOI: 10.1109/OJSP.2023.3337721

Tobias Kabzinski;Peter Jax

{"title":"A Flexible Framework for Expectation Maximization-Based MIMO System Identification for Time-Variant Linear Acoustic Systems","authors":"Tobias Kabzinski;Peter Jax","doi":"10.1109/OJSP.2023.3337721","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3337721","url":null,"abstract":"Quasi-continuous system identification of time-variant linear acoustic systems can be applied in various audio signal processing applications when numerous acoustic transfer functions must be measured. A prominent application is measuring head-related transfer functions. We treat the underlying multiple-input-multiple-output (MIMO) system identification problem in a state-space model as a joint estimation problem for states, representing impulse responses, and state-space model parameters using the expectation maximization (EM) algorithm. We address limitations of prior work by imposing different model structures, especially for dependencies within a (transformed) state vector. This results in block diagonal matrix structures, for which we derive M-step update rules. Making assumptions about this model structure and choosing a block size for a given application define the computational complexity. In examples, we found that applying this framework yields improvements of up to 10 dB in relative system distance in comparison to a conventional method.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"112-121"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060325","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

Synthbuster: Towards Detection of Diffusion Model Generated Images Synthbuster：检测扩散模型生成的图像

IEEE open journal of signal processing Pub Date : 2023-11-29 DOI: 10.1109/OJSP.2023.3337714

Quentin Bammey

{"title":"Synthbuster: Towards Detection of Diffusion Model Generated Images","authors":"Quentin Bammey","doi":"10.1109/OJSP.2023.3337714","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3337714","url":null,"abstract":"Synthetically-generated images are getting increasingly popular. Diffusion models have advanced to the stage where even non-experts can generate photo-realistic images from a simple text prompt. They expand creative horizons but also open a Pandora's box of potential disinformation risks. In this context, the present corpus of synthetic image detection techniques, primarily focusing on older generative models like Generative Adversarial Networks, finds itself ill-equipped to deal with this emerging trend. Recognizing this challenge, we introduce a method specifically designed to detect synthetic images produced by diffusion models. Our approach capitalizes on the inherent frequency artefacts left behind during the diffusion process. Spectral analysis is used to highlight the artefacts in the Fourier transform of a residual image, which are used to distinguish real from fake images. The proposed method can detect diffusion-model-generated images even under mild \u0000<sc>jpeg</small>\u0000 compression, and generalizes relatively well to unknown models. By pioneering this novel approach, we aim to fortify forensic methodologies and ignite further research into the detection of AI-generated images.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060181","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

Principled Pruning of Bayesian Neural Networks Through Variational Free Energy Minimization 通过变式自由能最小化对贝叶斯神经网络进行原则性修剪

IEEE open journal of signal processing Pub Date : 2023-11-29 DOI: 10.1109/OJSP.2023.3337718

Jim Beckers;Bart Van Erp;Ziyue Zhao;Kirill Kondrashov;Bert De Vries

{"title":"Principled Pruning of Bayesian Neural Networks Through Variational Free Energy Minimization","authors":"Jim Beckers;Bart Van Erp;Ziyue Zhao;Kirill Kondrashov;Bert De Vries","doi":"10.1109/OJSP.2023.3337718","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3337718","url":null,"abstract":"Bayesian model reduction provides an efficient approach for comparing the performance of all nested sub-models of a model, without re-evaluating any of these sub-models. Until now, Bayesian model reduction has been applied mainly in the computational neuroscience community on simple models. In this paper, we formulate and apply Bayesian model reduction to perform principled pruning of Bayesian neural networks, based on variational free energy minimization. Direct application of Bayesian model reduction, however, gives rise to approximation errors. Therefore, a novel iterative pruning algorithm is presented to alleviate the problems arising with naive Bayesian model reduction, as supported experimentally on the publicly available UCI datasets for different inference algorithms. This novel parameter pruning scheme solves the shortcomings of current state-of-the-art pruning methods that are used by the signal processing community. The proposed approach has a clear stopping criterion and minimizes the same objective that is used during training. Next to these benefits, our experiments indicate better model performance in comparison to state-of-the-art pruning schemes.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"195-203"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060256","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 Tiny Transformer-Based Anomaly Detection Framework for IoT Solutions 基于微型变压器的物联网解决方案异常检测框架

IEEE open journal of signal processing Pub Date : 2023-11-16 DOI: 10.1109/OJSP.2023.3333756

Luca Barbieri;Mattia Brambilla;Mario Stefanutti;Ciro Romano;Niccolò De Carlo;Manuel Roveri

{"title":"A Tiny Transformer-Based Anomaly Detection Framework for IoT Solutions","authors":"Luca Barbieri;Mattia Brambilla;Mario Stefanutti;Ciro Romano;Niccolò De Carlo;Manuel Roveri","doi":"10.1109/OJSP.2023.3333756","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3333756","url":null,"abstract":"The widespread proliferation of Internet of Things (IoT) devices has pushed for the development of novel transformer-based Anomaly Detection (AD) tools for an accurate monitoring of functionalities in industrial systems. Despite their outstanding performances, transformer models often rely on large Neural Networks (NNs) that are difficult to be executed by IoT devices due to their energy/computing constraints. This paper focuses on introducing tiny transformer-based AD tools to make them viable solutions for on-device AD. Starting from the state-of-the-art Anomaly Transformer (AT) model, which has been shown to provide accurate AD functionalities but it is characterized by high computational and memory demand, we propose a tiny AD framework that finds an optimized configuration of the AT model and uses it for devising a compressed version compatible with resource-constrained IoT systems. A knowledge distillation tool is developed to obtain a highly compressed AT model without degrading the AD performance. The proposed framework is firstly analyzed on four widely-adopted AD datasets and then assessed using data extracted from a real-world monitoring facility. The results show that the tiny AD tool provides a compressed AT model with a staggering 99.93% reduction in the number of trainable parameters compared to the original implementation (from 4.8 million to 3300 or 1400 according to the input dataset), without significantly compromising the accuracy in AD. Moreover, the compressed model substantially outperforms a popular Recurrent Neural Network (RNN)-based AD tool having a similar number of trainable weights as well as a conventional One-Class Support Vector Machine (OCSVM) algorithm.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"4 ","pages":"462-478"},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10319782","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138502120","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

Multichannel Acoustic Echo Cancellation With Beamforming in Dynamic Environments 动态环境下波束形成的多通道声回波消除

IEEE open journal of signal processing Pub Date : 2023-11-08 DOI: 10.1109/OJSP.2023.3331228

Yuval Konforti;Israel Cohen;Baruch Berdugo

{"title":"Multichannel Acoustic Echo Cancellation With Beamforming in Dynamic Environments","authors":"Yuval Konforti;Israel Cohen;Baruch Berdugo","doi":"10.1109/OJSP.2023.3331228","DOIUrl":"10.1109/OJSP.2023.3331228","url":null,"abstract":"Acoustic echo cancellers are integrated into various speech communication devices, such as hands-free conferencing systems and speakerphones. Microphone arrays can be employed to enhance the performance of such systems, though they assume a static environment when transitioning to double-talk, and rely on double-talk detection. This work introduces a multichannel echo canceller implemented by a microphone array beamformer that can adapt to a changing environment where the locations of both the far-end and near-end sources change during double-talk, with no double-talk detector. This is done by utilizing multiple recent frames in the short-time Fourier transform (STFT) domain. We show how can the acoustic paths be accurately estimated given the recent time frames of the far-end and microphone signals. Also, our beamformer aims to reduce background noise. Simulations are conducted in a reverberant room with nonlinear loudspeaker distortion and realistic low signal-to-echo ratio (SER) resembling a speakerphone. The experiments demonstrate the advantages of the proposed approach compared to normalized least-mean-squares (NLMS) based approaches.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"4 ","pages":"479-488"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10312825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135559903","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