2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)最新文献_第9页

Enhancing Human Activity Recognition Through Sensor Fusion And Hybrid Deep Learning Model 通过传感器融合和混合深度学习模型增强人体活动识别

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193698

A. Tarekegn, M. Ullah, F. A. Cheikh, Muhammad Sajjad

引用次数: 1

Attention-Based Convolutional Neural Network for CT Scan COVID-19 Detection 基于注意力的卷积神经网络CT扫描COVID-19检测

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193471

Alessia Rondinella, Francesco Guarnera, O. Giudice, A. Ortis, F. Rundo, S. Battiato

{"title":"Attention-Based Convolutional Neural Network for CT Scan COVID-19 Detection","authors":"Alessia Rondinella, Francesco Guarnera, O. Giudice, A. Ortis, F. Rundo, S. Battiato","doi":"10.1109/ICASSPW59220.2023.10193471","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193471","url":null,"abstract":"The accurate detection of Covid-19 from chest Computed Tomography (CT) images can assist in early diagnosis and management of the disease. This paper presents a solution for Covid-19 detection, presented in the challenge of 3rd Covid-19 competition, inside the “AI-enabled Medical Image Analysis Workshop” organized by IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) 2023. In this work, the application of deep learning models for chest CT image analysis was investigated, focusing on the use of a ResNet as a backbone network augmented with attention mechanisms. The ResNet provides an effective feature extractor for the classification task, while the attention mechanisms improve the model’s ability to focus on important regions of interest within the images. We conducted extensive experiments on a provided dataset and achieved a macro F1 score of 0.78 on the test set, demonstrating the potential to assist the diagnosis of Covid-19. Our proposed approach leverages the power of deep learning with attention mechanisms to address the challenges of Covid-19 detection in the early detection and management of the disease. In both test and validation set, the proposed method outperformed the baseline of the challenge, ranking fifth in the competition.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115605306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Generative Models For Large-Scale Simulations Of Connectome Development 大规模连接体发育模拟的生成模型

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193544

Skylar J. Brooks, C. Stamoulis

{"title":"Generative Models For Large-Scale Simulations Of Connectome Development","authors":"Skylar J. Brooks, C. Stamoulis","doi":"10.1109/ICASSPW59220.2023.10193544","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193544","url":null,"abstract":"Functional interactions and anatomic connections between brain regions form the connectome. Its mathematical representation in terms of a graph reflects the inherent neuroanatomical organization into structures and regions (nodes) that are interconnected through neural fiber tracts and/or interact functionally (edges). Without knowledge of the ground truth topology of the connectome, functional (directional or nondirectional) graphs represent estimates of signal correlations, from which underlying mechanisms and processes, such as development and aging, or neuropathologies, are difficult to unravel. Biologically meaningful simulations using synthetic graphs with controllable parameters can complement real data analyses and provide critical insights into mechanisms underlying the organization of the connectome. Generative models can be highly valuable tools for creating large datasets of synthetic graphs with known topological characteristics. However, for these graphs to be meaningful, the variation of model parameters needs to be driven by real data. This paper presents a novel, data-driven approach for tuning the parameters of the generative LancichinettiFortunato-Radicchi (LFR) model, using a large dataset of connectomes (n = 5566) estimated from resting-state fMRI from early adolescents in the historically large Adolescent Brain Cognitive Development Study (ABCD). It also presents an application, i.e., simulations using the LFR, to generate large datasets of synthetic graphs representing brains at different stages of neural maturation, and gain insights into developmental changes in their topological organization.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122619217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Querying A Sign Language Dictionary with Videos Using Dense Vector Search 使用密集向量搜索查询视频手语词典

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193531

Mathieu De Coster, J. Dambre

引用次数: 0

Easier Notation – a Proposal for a Gloss-Based Scripting Language for Sign Language Generation Based on Lexical Data 更容易的符号-基于词汇数据的手语生成基于术语表的脚本语言的建议

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10192997

Thomas Hanke, Lutz König, Reiner Konrad, Maria Kopf, Marc Schulder, Rosalee J. Wolfe

引用次数: 1

On Hybrid Free-Space Optic-Radio Systems as Enablers of 6G Services Over Non-Terrestrial Networks 在非地面网络上实现6G业务的混合自由空间光无线电系统

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193454

M. Amay, J. Bas

引用次数: 0

Role of Audio In Video Summarization 音频在视频总结中的作用

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10192578

Ibrahim Shoer, Berkay Köprü, E. Erzin

{"title":"Role of Audio In Video Summarization","authors":"Ibrahim Shoer, Berkay Köprü, E. Erzin","doi":"10.1109/ICASSPW59220.2023.10192578","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10192578","url":null,"abstract":"Video summarization attracts attention for efficient video representation, retrieval, and browsing to ease volume and traffic surge problems. Although video summarization mostly uses the visual channel for compaction, the benefits of audio-visual modeling appeared in recent literature. The information coming from the audio channel can be a result of audio-visual correlation in the video content. In this study, we propose a new audio-visual video summarization framework integrating four ways of audio-visual information fusion with GRU-based and attention-based networks. Furthermore, we investigate a new explainability methodology using audio-visual canonical correlation analysis (CCA) to better understand and explain the role of audio in the video summarization task. Experimental evaluations on the TVSum dataset attain F1 score and Kendall-tau score improvements for the audio-visual video summarization. Furthermore, splitting video content on TVSum and COGNIMUSE datasets based on audio-visual CCA as positively and negatively correlated videos yields a strong performance improvement over the positively correlated videos for audio-only and audio-visual video summarization.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125975376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of Driver’s Stress Affection in Simulated Autonomous Driving Scenarios 模拟自动驾驶场景下驾驶员应力影响预测

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193353

Valerio De Caro, Herbert Danzinger, C. Gallicchio, Clemens Könczöl, Vincenzo Lomonaco, Mina Marmpena, S. Politi, O. Veledar, D. Bacciu

引用次数: 0

An Improved Autofocus Algorithm With Bayesian Tracking of Residual Motion For Automotive MIMO-SAR Imaging 基于贝叶斯残馀运动跟踪的汽车MIMO-SAR自动对焦改进算法

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193027

Gabriele Balducci, M. Manzoni, S. Tebaldini, A. M. Guarnieri, C. Prati, Ivan Russo

{"title":"An Improved Autofocus Algorithm With Bayesian Tracking of Residual Motion For Automotive MIMO-SAR Imaging","authors":"Gabriele Balducci, M. Manzoni, S. Tebaldini, A. M. Guarnieri, C. Prati, Ivan Russo","doi":"10.1109/ICASSPW59220.2023.10193027","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193027","url":null,"abstract":"Automotive Synthetic Aperture Radar (SAR) is a promising technology for autonomous driving, where reliable perception of the environment is needed. Though, SAR focusing needs precise vehicle’s trajectory knowledge, not compatible with automotive-grade navigation systems. Current autofocus algorithms refine navigation-based trajectory with radar data but do not exploit vehicle’s dynamic in the residual motion estimation. This paper investigates the injection of a-priori knowledge into residual motion estimation to achieve improved and physically consistent SAR imaging. An autoregressive model of the residual velocities and Bayesian tracking via Kalman Filter are proposed and deeply studied upon application on real data acquired in an open road campaign. A new metric is introduced to quantitatively compare the outcomes: the variance of Hough lines angular coefficients. Experimental results confirm that the metric is informative, and the presence of memory in the residual motion estimation is effective in better estimating residual velocity and, consequently, improved SAR imaging.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130332512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Source Localization and Functional Connectivity Estimation With State-Space Models: Preliminary Feasibility Analysis 基于状态空间模型的动态源定位和功能连通性估计:初步可行性分析

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193527

J. Bornot, R. Sotero, D. Coyle

{"title":"Dynamic Source Localization and Functional Connectivity Estimation With State-Space Models: Preliminary Feasibility Analysis","authors":"J. Bornot, R. Sotero, D. Coyle","doi":"10.1109/ICASSPW59220.2023.10193527","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193527","url":null,"abstract":"Dynamic imaging of source and functional connectivity (FC) using electroencephalographic (EEG) signals is essential for understanding the brain and cognition with sufficiently affordable technology to be widely applicable for studying changes associated with healthy ageing and the progression of neuropathology. We present an application for group analysis of recently developed state-space models and algorithms for simultaneously estimating the large-scale EEG inverse and FC problems. This approach reduces estimation bias and facilitates a detailed exploration and investigation of neuronal dynamics compared to current techniques. We present feasibility analyses for simulated and real EEG event-related data. The latter analysis uses a sixteen subjects EEG (Wakeman and Henson’s) database, with signals recorded during a face-processing task. We implement a state-space methodology efficiently using an alternating least squares (ALS) algorithm. This application to neuroimaging analysis may be critical to reliably capture the brain dynamics despite interindividual variability, as demonstrated by the results presented.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130453331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1