IEEE open journal of signal processing最新文献

{"title":"Towards Automated Seizure Detection With Wearable EEG – Grand Challenge","authors":"Miguel Bhagubai;Lauren Swinnen;Evy Cleeren;Wim Van Paesschen;Maarten De Vos;Christos Chatzichristos","doi":"10.1109/OJSP.2024.3378604","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3378604","url":null,"abstract":"The diagnosis of epilepsy can be confirmed in-hospital via video-electroencephalography (vEEG). Currently, long-term monitoring is limited to self-reporting seizure occurrences by the patients. In recent years, the development of wearable sensors has allowed monitoring patients outside of specialized environments. The application of wearable EEG devices for monitoring epileptic patients in ambulatory environments is still dampened by the low performance achieved by automated seizure detection frameworks. In this work, we present the results of a seizure detection grand challenge, organized as an attempt to stimulate the development of automated methodologies for detection of seizures on wearable EEG. The main drawbacks for developing wearable EEG seizure detection algorithms is the lack of data needed for training such frameworks. In this challenge, we provided participants with a large dataset of 42 patients with focal epilepsy, containing continuous recordings of behind-the-ear (bte) EEG. We challenged participants to develop a robust seizure classifier based on wearable EEG. Additionally, we proposed a subtask in order to motivate data-centric approaches to improve the training and performance of seizure detection models. An additional dataset, containing recordings with a bte-EEG wearable device, was employed to evaluate the work submitted by participants. In this paper, we present the five best scoring methodologies. The best performing approach was a feature-based decision tree ensemble algorithm with data augmentation via Fourier Transform surrogates. The organization of this challenge is of high importance for improving automated EEG analysis for epilepsy diagnosis, working towards implementing these technologies in clinical practice.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"717-724"},"PeriodicalIF":2.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447983","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":"REM-U-Net: Deep Learning Based Agile REM Prediction With Energy-Efficient Cell-Free Use Case","authors":"Hazem Sallouha;Shamik Sarkar;Enes Krijestorac;Danijela Cabric","doi":"10.1109/OJSP.2024.3378591","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3378591","url":null,"abstract":"Radio environment maps (REMs) hold a central role in optimizing wireless network deployment, enhancing network performance, and ensuring effective spectrum management. Conventional REM prediction methods are either excessively time-consuming, e.g., ray tracing, or inaccurate, e.g., statistical models, limiting their adoption in modern inherently dynamic wireless networks. Deep learning-based REM prediction has recently attracted considerable attention as an appealing, accurate, and time-efficient alternative. However, existing works on REM prediction using deep learning are either confined to 2D maps or use a relatively small dataset. In this paper, we introduce a runtime-efficient REM prediction framework based on U-Nets, trained on a large-scale 3D maps dataset. In addition, data preprocessing steps are investigated to further refine the REM prediction accuracy. The proposed U-Net framework, along with preprocessing steps, are evaluated in the context of \u0000<italic>the 2023 IEEE ICASSP Signal Processing Grand Challenge, namely, the First Pathloss Radio Map Prediction Challenge</i>\u0000. The evaluation results demonstrate that the proposed method achieves an average normalized root-mean-square error (RMSE) of 0.045 with an average of 14 milliseconds (ms) runtime. Finally, we position our achieved REM prediction accuracy in the context of a relevant cell-free massive multiple-input multiple-output (CF-mMIMO) use case. We demonstrate that one can obviate consuming energy on large-scale fading (LSF) measurements and rely on predicted REM instead to decide which sleep access points (APs) to switch on in a CF-mMIMO network that adopts a minimum propagation loss AP switch ON/OFF strategy.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"750-765"},"PeriodicalIF":2.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447987","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":"ICASSP 2023 Acoustic Echo Cancellation Challenge","authors":"Ross Cutler;Ando Saabas;Tanel Pärnamaa;Marju Purin;Evgenii Indenbom;Nicolae-Cătălin Ristea;Jegor Gužvin;Hannes Gamper;Sebastian Braun;Robert Aichner","doi":"10.1109/OJSP.2024.3376289","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3376289","url":null,"abstract":"The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is still a top issue in audio communication. This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic + buffering latency to 20 ms, as well as including a full-band version of AECMOS (Purin et al., 2020). We open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 10,000 real audio devices and human speakers in real environments, as well as a synthetic dataset. We open source an online subjective test framework and provide an objective metric for researchers to quickly test their results. The winners of this challenge were selected based on the average mean opinion score (MOS) achieved across all scenarios and the word accuracy (WAcc) rate.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"675-685"},"PeriodicalIF":2.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10472289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447984","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 Combined Channel Estimation and Optimal Uplink Receive Combining for User- Centric Cell-Free Massive MIMO Systems","authors":"Robbe Van Rompaey;Marc Moonen","doi":"10.1109/OJSP.2024.3377098","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3377098","url":null,"abstract":"Cell-free massive MIMO (CFmMIMO) is considered as one of the enablers to meet the demand for increasing data rates of next generation (6G) wireless communications. In user-centric CFmMIMO, each user equipment (UE) is served by a user-selected set of surrounding access points (APs), requiring efficient signal processing algorithms minimizing inter-AP communications, while still providing a good quality of service to all UEs. This paper provides algorithms for channel estimation (CE) and uplink (UL) receive combining (RC), designed for CFmMIMO channels using different assumptions on the structure of the channel covariances. Three different channel models are considered: line-of-sight (LoS) channels, non-LoS (NLoS) channels (the common Rayleigh fading model) and a combination of LoS and NLoS channels (the general Rician fading model). The LoS component introduces correlation between the channels at different APs that can be exploited to improve the CE and the RC. The channel estimates and receive combiners are obtained in each AP by processing the local antenna signals of the AP, together with compressed versions of all the other antenna signals of the APs serving the UE, during UL training. To make the proposed method scalable, the distributed user-centric channel estimation and receive combining (DUCERC) algorithm is presented that significantly reduces the necessary communications between the APs. The effectiveness of the proposed method and algorithm is demonstrated via numerical simulations.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"559-576"},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10472081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140648034","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":"Robust Regularized Locality Preserving Indexing for Fiedler Vector Estimation","authors":"Aylin Taştan;Michael Muma;Abdelhak M. Zoubir","doi":"10.1109/OJSP.2024.3400683","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3400683","url":null,"abstract":"The Fiedler vector is the eigenvector associated with the algebraic connectivity of the graph Laplacian. It is central to graph analysis as it provides substantial information to learn the latent structure of a graph. In real-world applications, however, the data may be subject to heavy-tailed noise and outliers which deteriorate the structure of the Fiedler vector estimate and lead to a breakdown of popular methods. Thus, we propose a Robust Regularized Locality Preserving Indexing (RRLPI) Fiedler vector estimation method that approximates the nonlinear manifold structure of the Laplace Beltrami operator while minimizing the impact of outliers. To achieve this aim, an analysis of the effects of two fundamental outlier types on the eigen-decomposition of block affinity matrices is conducted. Then, an error model is formulated based on which the RRLPI method is developed. It includes an unsupervised regularization parameter selection algorithm that leverages the geometric structure of the projection space. The performance is benchmarked against existing methods in terms of detection probability, partitioning quality, image segmentation capability, robustness and computation time using a large variety of synthetic and real data experiments.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"867-885"},"PeriodicalIF":2.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10530068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965021","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":"L3DAS23: Learning 3D Audio Sources for Audio-Visual Extended Reality","authors":"Riccardo F. Gramaccioni;Christian Marinoni;Changan Chen;Aurelio Uncini;Danilo Comminiello","doi":"10.1109/OJSP.2024.3376297","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3376297","url":null,"abstract":"The primary goal of the L3DAS (Learning 3D Audio Sources) project is to stimulate and support collaborative research studies concerning machine learning techniques applied to 3D audio signal processing. To this end, the L3DAS23 Challenge, presented at IEEE ICASSP 2023, focuses on two spatial audio tasks of paramount interest for practical uses: 3D speech enhancement (3DSE) and 3D sound event localization and detection (3DSELD). Both tasks are evaluated within augmented reality applications. The aim of this paper is to describe the main results obtained from this challenge. We provide the L3DAS23 dataset, which comprises a collection of first-order Ambisonics recordings in reverberant simulated environments. Indeed, we maintain some general characteristics of the previous L3DAS challenges, featuring a pair of first-order Ambisonics microphones to capture the audio signals and involving multiple-source and multiple-perspective Ambisonics recordings. However, in this new edition, we introduce audio-visual scenarios by including images that depict the frontal view of the environments as captured from the perspective of the microphones. This addition aims to enrich the challenge experience, giving participants tools for exploring a combination of audio and images for solving the 3DSE and 3DSELD tasks. In addition to a brand-new dataset, we provide updated baseline models designed to take advantage of audio-image pairs. To ensure accessibility and reproducibility, we also supply supporting API for an effortless replication of our results. Lastly, we present the results achieved by the participants of the L3DAS23 Challenge.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"632-640"},"PeriodicalIF":2.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10468560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447968","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":"Auditory EEG Decoding Challenge for ICASSP 2023","authors":"Mohammad Jalilpour Monesi;Lies Bollens;Bernd Accou;Jonas Vanthornhout;Hugo Van Hamme;Tom Francart","doi":"10.1109/OJSP.2024.3376296","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3376296","url":null,"abstract":"This paper describes the auditory EEG challenge, organized as one of the Signal Processing Grand Challenges at ICASSP 2023. The challenge provides EEG recordings of 85 subjects who listened to continuous speech, as audiobooks or podcasts, while their brain activity was recorded. EEG recordings of 71 subjects were provided as a training set such that challenge participants could train their models on a relatively large dataset. The remaining 14 subjects were used as held-out subjects in evaluating the challenge. The challenge consists of two tasks that relate electroencephalogram (EEG) signals to the presented speech stimulus. The first task, match-mismatch, aims to determine which of two speech segments induced a given EEG segment. In the second regression task, the goal is to reconstruct the speech envelope from the EEG. For the match-mismatch task, the performance of different teams was close to the baseline model, and the models did generalize well to unseen subjects. In contrast, For the regression task, the top teams significantly improved over the baseline models in the held-out stories test set while failing to generalize to unseen subjects.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"652-661"},"PeriodicalIF":2.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10468639","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447999","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":"Person Identification and Relapse Detection From Continuous Recordings of Biosignals Challenge: Overview and Results","authors":"Athanasia Zlatintsi;Panagiotis P. Filntisis;Niki Efthymiou;Christos Garoufis;George Retsinas;Thomas Sounapoglou;Ilias Maglogiannis;Panayiotis Tsanakas;Nikolaos Smyrnis;Petros Maragos","doi":"10.1109/OJSP.2024.3376300","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3376300","url":null,"abstract":"This paper presents an overview of the e-Prevention: Person Identification and Relapse Detection Challenge, which was an open call for researchers at ICASSP-2023. The challenge aimed at the analysis and processing of long-term continuous recordings of biosignals recorded from wearable sensors, namely accelerometers, gyroscopes and heart rate monitors embedded in smartwatches, as well as sleep information and daily step counts, in order to extract high-level representations of the wearer's activity and behavior, termed as digital phenotypes. Specifically, with the goal of analyzing the ability of these digital phenotypes in quantifying behavioral patterns, two tasks were evaluated in two distinct tracks: 1) Identification of the wearer of the smartwatch, and 2) Detection of psychotic relapses in patients in the psychotic spectrum. The long-term data that have been used in this challenge have been acquired during the course of the e-Prevention project (Zlatintsi et al., 2022), an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. Two baseline systems, one for each task, were described and the validation scores for both tasks were provided to the participants. Herein, we present an overview of the approaches and methods as well as the performance analysis and the results of the 5-top ranked participating teams, which in track 1 achieved accuracy results between 91%-95%, while in track 2 mean PR- and ROC-AUC scores between 0.6051 and 0.6489 were obtained. Finally, we also make the datasets publicly available at \u0000<uri>https://robotics.ntua.gr/eprevention-sp-challenge/</uri>\u0000.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"641-651"},"PeriodicalIF":2.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10470363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447969","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":"ICASSP 2023 Speech Signal Improvement Challenge","authors":"Ross Cutler;Ando Saabas;Babak Naderi;Nicolae-Cătălin Ristea;Sebastian Braun;Solomiya Branets","doi":"10.1109/OJSP.2024.3376293","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3376293","url":null,"abstract":"The ICASSP 2023 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. The speech signal quality can be measured with SIG in ITU-T P.835 and is still a top issue in audio communication and conferencing systems. For example, in the ICASSP 2022 Deep Noise Suppression challenge, the improvement in the background and overall quality is impressive, but the improvement in the speech signal is not statistically significant. To improve the speech signal the following speech impairment areas must be addressed: coloration, discontinuity, loudness, reverberation, and noise. A training and test set was provided for the challenge, and the winners were determined using an extended crowdsourced implementation of ITU-T P.804’s listening phase. The results show significant improvement was made across all measured dimensions of speech quality.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"662-674"},"PeriodicalIF":2.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10470433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448002","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":"Spatial Sigma-Delta Modulation for Coarsely Quantized Massive MIMO Downlink: Flexible Designs by Convex Optimization","authors":"Wai-Yiu Keung;Wing-Kin Ma","doi":"10.1109/OJSP.2024.3375653","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3375653","url":null,"abstract":"This article considers the context of multiuser massive MIMO downlink precoding with low-resolution digital-to-analog converters (DACs) at the transmitter. This subject is motivated by the consideration that it is expensive to employ high-resolution DACs for practical massive MIMO implementations. The challenge with using low-resolution DACs is to overcome the detrimental quantization error effects. Recently, spatial Sigma-Delta (\u0000<inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula>\u0000) modulation has arisen as a viable way to put quantization errors under control. This approach takes insight from temporal \u0000<inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula>\u0000 modulation in classical DAC studies. Assuming a 1D uniform linear transmit antenna array, the principle is to shape the quantization errors in space such that the shaped quantization errors are pushed away from the user-serving angle sector. In the previous studies, spatial \u0000<inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula>\u0000 modulation was performed by direct application of the basic first- and second-order modulators from the \u0000<inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula>\u0000 literature. In this paper, we develop a general \u0000<inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula>\u0000 modulator design framework for any given order, for any given number of quantization levels, and for any given angle sector. We formulate our design as a problem of maximizing the signal-to-quantization-and-noise ratios (SQNRs) experienced by the users. The formulated problem is convex and can be efficiently solved by available solvers. Our proposed framework offers the alternative option of focused quantization error suppression in accordance with channel state information. Our framework can also be extended to 2D planar transmit antenna arrays. We perform numerical study under different operating conditions, and the numerical results suggest that, given a moderate number of quantization levels, say, 5 to 7 levels, our optimization-based \u0000<inline-formula><tex-math>$Sigma Delta$</tex-math></inline-formula>\u0000 modulation schemes can lead to bit error rate performance close to that of the unquantized counterpart.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"520-538"},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10465600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621199","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}