ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第10页

Laplacian Regularized Tensor Low-Rank Minimization for Hyperspectral Snapshot Compressive Imaging 高光谱快照压缩成像的拉普拉斯正则张量低秩最小化

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9413381

Yi Yang, Fei Jiang, Hongtao Lu

{"title":"Laplacian Regularized Tensor Low-Rank Minimization for Hyperspectral Snapshot Compressive Imaging","authors":"Yi Yang, Fei Jiang, Hongtao Lu","doi":"10.1109/ICASSP39728.2021.9413381","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9413381","url":null,"abstract":"Snapshot Compressive Imaging (SCI) systems, including hyperspectral compressive imaging and video compressive imaging, are designed to depict high-dimensional signals with limited data by mapping multiple images into one. One key module of SCI systems is a high quality reconstruction algorithm for original frames. However, most existing decoding algorithms are based on vectorization representation and fail to capture the intrinsic structural information of high dimensional signals. In this paper, we propose a tensor-based low-rank reconstruction algorithm with hyper-Laplacian constraint for hyperspectral SCI systems. First, we integrate the non-local self-similarity and tensor low-rank minimization approach to explore the intrinsic structural correlations along spatial and spectral domains. Then, we introduce a hyper-Laplacian constraint to model the global spectral structures, alleviating the ringing artifacts in the spatial domain. Experimental results on hyperspectral image corpus demonstrate the proposed algorithm achieves average 0.8~2.9 dB improvement in PSNR over state-of-the-art work.","PeriodicalId":347060,"journal":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125237680","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

A High-Frame-Rate Eye-Tracking Framework for Mobile Devices 移动设备的高帧率眼动追踪框架

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414624

Yuhu Chang, Changyang He, Yingying Zhao, T. Lu, Ning Gu

{"title":"A High-Frame-Rate Eye-Tracking Framework for Mobile Devices","authors":"Yuhu Chang, Changyang He, Yingying Zhao, T. Lu, Ning Gu","doi":"10.1109/ICASSP39728.2021.9414624","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9414624","url":null,"abstract":"Gaze-on-screen tracking, an appearance-based eye-tracking task, has drawn significant interest in recent years. While learning-based high-precision eye-tracking methods have been designed in the past, the complex pre-training and high computation in neural network-based deep models restrict their applicability in mobile devices. Moreover, as the display frame rate of mobile devices has steadily increased to 120 fps, high-frame-rate eye tracking becomes increasingly challenging. In this work, we tackle the tracking efficiency challenge and introduce GazeHFR, a biologic-inspired eye-tracking model specialized for mobile devices, offering both high accuracy and efficiency. Specifically, GazeHFR classifies the eye movement into two distinct phases, i.e., saccade and smooth pursuit, and leverages inter-frame motion information combined with lightweight learning models tailored to each movement phase to deliver high-efficient eye tracking without affecting accuracy. Compared to prior art, Gaze-HFR achieves approximately 7x speedup and 15% accuracy improvement on mobile devices.","PeriodicalId":347060,"journal":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130788616","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

Predictive Coding for Lossless Dataset Compression 无损数据集压缩的预测编码

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9413447

Madeleine Barowsky, Alexander Mariona, F. Calmon

引用次数: 2

Real-Time Speech Enhancement for Mobile Communication Based on Dual-Channel Complex Spectral Mapping 基于双通道复杂频谱映射的移动通信实时语音增强

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414346

Ke Tan, Xueliang Zhang, Deliang Wang

引用次数: 3

A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants 关于Weisfeiler-Lehman测试及其变体的简短教程

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9413523

Ningyuan Huang, Soledad Villar

引用次数: 25

Leveraging A Multiple-Strain Model with Mutations in Analyzing the Spread of Covid-19 利用多株突变模型分析Covid-19的传播

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414595

Anirudh Sridhar, Osman Yağan, Rashad M. Eletreby, S. Levin, J. Plotkin, H. Poor

{"title":"Leveraging A Multiple-Strain Model with Mutations in Analyzing the Spread of Covid-19","authors":"Anirudh Sridhar, Osman Yağan, Rashad M. Eletreby, S. Levin, J. Plotkin, H. Poor","doi":"10.1109/ICASSP39728.2021.9414595","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9414595","url":null,"abstract":"The spread of COVID-19 has been among the most devastating events affecting the health and well-being of humans worldwide since World War II. A key scientific goal concerning COVID-19 is to develop mathematical models that help us to understand and predict its spreading behavior, as well as to provide guidelines on what can be done to limit its spread. In this paper, we discuss how our recent work on a multiple-strain spreading model with mutations can help address some key questions concerning the spread of COVID-19. We highlight the recent reports on a mutation of SARS-CoV-2 that is thought to be more transmissible than the original strain and discuss the importance of incorporating mutation and evolutionary adaptations (together with the network structure) in epidemic models. We also demonstrate how the multiple-strain transmission model can be used to assess the effectiveness of mask-wearing in limiting the spread of COVID-19. Finally, we present simulation results to demonstrate our ideas and the utility of the multiple-strain model in the context of COVID-19.","PeriodicalId":347060,"journal":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133537653","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: 10

Quickest Joint Detection and Classification of Faults in Statistically Periodic Processes 统计周期过程中故障的快速联合检测与分类

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414101

T. Banerjee, Smruti Padhy, A. Taha, E. John

引用次数: 0

Neural Utterance Confidence Measure for RNN-Transducers and Two Pass Models rnn换能器和两通道模型的神经话语置信度度量

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414467

Ashutosh Gupta, Ankur Kumar, Dhananjaya N. Gowda, Kwangyoun Kim, Sachin Singh, Shatrughan Singh, Chanwoo Kim

引用次数: 0

Low-Latency Polar Decoder Using Overlapped SCL Processing 使用重叠SCL处理的低延迟极性解码器

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9414326

D. Kam, B. Y. Kong, Youngjoo Lee

引用次数: 2

Deep Generative Demixing: Error Bounds for Demixing Subgaussian Mixtures of Lipschitz Signals 深度生成解混:Lipschitz信号亚高斯混合解混的误差界

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2021-06-06 DOI: 10.1109/ICASSP39728.2021.9413573

Aaron Berk

{"title":"Deep Generative Demixing: Error Bounds for Demixing Subgaussian Mixtures of Lipschitz Signals","authors":"Aaron Berk","doi":"10.1109/ICASSP39728.2021.9413573","DOIUrl":"https://doi.org/10.1109/ICASSP39728.2021.9413573","url":null,"abstract":"Generative neural networks (GNNs) have gained renown for efficaciously capturing intrinsic low-dimensional structure in natural images. Here, we investigate the subgaussian demixing problem for two Lipschitz signals, with GNN demixing as a special case. In demixing, one seeks identification of two signals given their sum and prior structural information. Here, we assume each signal lies in the range of a Lipschitz function, which includes many popular GNNs as a special case. We prove a sample complexity bound for nearly optimal recovery error that extends a recent result of Bora, et al. (2017) from the compressed sensing setting with gaussian matrices to demixing with subgaussian ones. Under a linear signal model in which the signals lie in convex sets, McCoy & Tropp (2014) have characterized the sample complexity for identification under subgaussian mixing. In the present setting, the signal structure need not be convex. For example, our result applies to a domain that is a non-convex union of convex cones. We support the efficacy of this demixing model with numerical simulations using trained GNNs, suggesting an algorithm that would be an interesting object of further theoretical study.","PeriodicalId":347060,"journal":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132751284","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: 2