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Gated Recurrent Networks for Video Super Resolution 用于视频超分辨率的门控循环网络
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287713
Santiago López-Tapia, Alice Lucas, R. Molina, A. Katsaggelos
{"title":"Gated Recurrent Networks for Video Super Resolution","authors":"Santiago López-Tapia, Alice Lucas, R. Molina, A. Katsaggelos","doi":"10.23919/Eusipco47968.2020.9287713","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287713","url":null,"abstract":"Despite the success of Recurrent Neural Networks in tasks involving temporal video processing, few works in Video Super-Resolution (VSR) have employed them. In this work we propose a new Gated Recurrent Convolutional Neural Network for VSR adapting some of the key components of a Gated Recurrent Unit. Our model employs a deformable attention module to align the features calculated at the previous time step with the ones in the current step and then uses a gated operation to combine them. This allows our model to effectively reuse previously calculated features and exploit longer temporal relationships between frames without the need of explicit motion compensation. The experimental validation shows that our approach outperforms current VSR learning based models in terms of perceptual quality and temporal consistency.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"13 1","pages":"700-704"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78371605","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
Signal Analysis Using Local Polynomial Approximations 用局部多项式逼近的信号分析
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287801
R. Wildhaber, Elizabeth Ren, F. Waldmann, Hans-Andrea Loeliger
{"title":"Signal Analysis Using Local Polynomial Approximations","authors":"R. Wildhaber, Elizabeth Ren, F. Waldmann, Hans-Andrea Loeliger","doi":"10.23919/Eusipco47968.2020.9287801","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287801","url":null,"abstract":"Local polynomial approximations represent a versatile feature space for time-domain signal analysis. The parameters of such polynomial approximations can be computed by efficient recursions using autonomous linear state space models and often allow analytical solutions for quantities of interest. The approach is illustrated by practical examples including the estimation of the delay difference between two acoustic signals and template matching in electrocardiogram signals with local variations in amplitude and time scale.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"52 6 1","pages":"2239-2243"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77790220","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: 3
Super-Resolution Time-of-Arrival Estimation using Neural Networks 基于神经网络的超分辨率到达时间估计
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287673
Yao-Shan Hsiao, Mingyu Yang, Hun-Seok Kim
{"title":"Super-Resolution Time-of-Arrival Estimation using Neural Networks","authors":"Yao-Shan Hsiao, Mingyu Yang, Hun-Seok Kim","doi":"10.23919/Eusipco47968.2020.9287673","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287673","url":null,"abstract":"This paper presents a learning-based algorithm that estimates the time of arrival (ToA) of radio frequency (RF) signals from channel frequency response (CFR) measurements for wireless localization applications. A generator neural network is proposed to enhance the effective bandwidth of the narrowband CFR measurement and to produce a high-resolution estimation of channel impulse response (CIR). In addition, two regressor neural networks are introduced to perform a two-step coarsefine ToA estimation based on the enhanced CIR. For simulated channels, the proposed method achieves 9% – 58% improved root mean squared error (RMSE) for distance ranging and up to 22% improved false detection rate compared with conventional super-resolution algorithms. For real-world measured channels, the proposed method exhibits an improvement of 1.3m in distance error at 90 percentile.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"35 1","pages":"1692-1696"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81689834","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: 6
Automatic Image Colorization based on Multi-Discriminators Generative Adversarial Networks 基于多鉴别器生成对抗网络的图像自动着色
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287792
Youssef Mourchid, M. Donias, Y. Berthoumieu
{"title":"Automatic Image Colorization based on Multi-Discriminators Generative Adversarial Networks","authors":"Youssef Mourchid, M. Donias, Y. Berthoumieu","doi":"10.23919/Eusipco47968.2020.9287792","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287792","url":null,"abstract":"This paper presents a deep automatic colorization approach which avoids any manual intervention. Recently Generative Adversarial Network (GANs) approaches have proven their effectiveness for image colorization tasks. Inspired by GANs methods, we propose a novel colorization model that produces more realistic quality results. The model employs an additional discriminator which works in the feature domain. Using a feature discriminator, our generator produces structural high-frequency features instead of noisy artifacts. To achieve the required level of details in the colorization process, we incorporate non-adversarial losses from recent image style transfer techniques. Besides, the generator architecture follows the general shape of U-Net, to transfer information more effectively between distant layers. The performance of the proposed model was evaluated quantitatively as well as qualitatively with places365 dataset. Results show that the proposed model achieves more realistic colors with less artifacts compared to the state-of-the-art approaches.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"23 1","pages":"1532-1536"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78857899","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
Distributed combined acoustic echo cancellation and noise reduction using GEVD-based distributed adaptive node specific signal estimation with prior knowledge 基于gevd的分布式自适应节点特定信号估计与先验知识的分布式组合声回波抵消与降噪
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287337
Santiago Ruiz, T. Waterschoot, M. Moonen
{"title":"Distributed combined acoustic echo cancellation and noise reduction using GEVD-based distributed adaptive node specific signal estimation with prior knowledge","authors":"Santiago Ruiz, T. Waterschoot, M. Moonen","doi":"10.23919/Eusipco47968.2020.9287337","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287337","url":null,"abstract":"Distributed combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor network (WASN) is tackled by using a specific version of the PK-GEVD-DANSE algorithm (cfr. [1]). Although this algorithm was initially developed for distributed NR with partial prior knowledge of the desired speech steering vector, it is shown that it can also be used for AEC combined with NR. Simulations have been carried out using centralized and distributed batch-mode implementations to verify the performance of the algorithm in terms of AEC quantified with the echo return loss enhancement (ERLE), as well as in terms of the NR quantified with the signal- to-noise ratio (SNR).","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"363 1","pages":"206-210"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76557926","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
Transmit Beampattern Synthesis for MIMO Radar with One-Bit DACs 基于位dac的MIMO雷达发射波束图合成
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287840
Tong Wei, B. Liao, Peng Xiao, Ziyang Cheng
{"title":"Transmit Beampattern Synthesis for MIMO Radar with One-Bit DACs","authors":"Tong Wei, B. Liao, Peng Xiao, Ziyang Cheng","doi":"10.23919/Eusipco47968.2020.9287840","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287840","url":null,"abstract":"In this paper, the problem of transmit beampattern synthesis (i.e., transmit beamforming) in multiple input multiple output (MIMO) radar which deploys one-bit digital-to-analog converts (DACs) is investigated. We aim to design appropriate transmit signal sequences, which are quantized by one-bit DACs, such that the amount of transmit energy can be focused into mainlobe region as much as possible, meanwhile, the leakage power of sidelobe region is minimized. It is shown that these requirements can be simultaneously fulfilled by minimizing the integrated sidelobe to mainlobe ratio (ISMR) of transmit beampattern with discrete binary constraints. According to this concept, we utilize the alternating direction multiplier method (ADMM) framework to solve the resulting nonconvex problem. Simulation results will demonstrate the effectiveness and improved performance of the proposed method.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"19 1","pages":"1827-1830"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87485898","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
A Graph Signal Processing Framework for the Classification of Temporal Brain Data 脑时态数据分类的图信号处理框架
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287486
Sarah Itani, D. Thanou
{"title":"A Graph Signal Processing Framework for the Classification of Temporal Brain Data","authors":"Sarah Itani, D. Thanou","doi":"10.23919/Eusipco47968.2020.9287486","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287486","url":null,"abstract":"Graph Signal Processing (GSP) addresses the analysis of data living on an irregular domain which can be modeled with a graph. This capability is of great interest for the study of brain connectomes. In this case, data lying on the nodes of the graph are considered as signals (e.g., fMRI time-series) that have a strong dependency on the graph topology (e.g., brain structural connectivity). In this paper, we adopt GSP tools to build features related to the frequency content of the signals. To make these features highly discriminative, we apply an extension of the Fukunaga-Koontz transform. We then use these new features to train a decision tree for the prediction of autism spectrum disorder. Interestingly, our framework outperforms state-of-the-art methods on the publicly available ABIDE dataset.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"38 1","pages":"1180-1184"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87088152","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: 9
Improving Energy Compaction of Adaptive Fourier Decomposition 改进自适应傅里叶分解的能量压缩
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287567
A. Borowicz
{"title":"Improving Energy Compaction of Adaptive Fourier Decomposition","authors":"A. Borowicz","doi":"10.23919/Eusipco47968.2020.9287567","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287567","url":null,"abstract":"Adaptive Fourier decomposition (AFD) provides an expansion of an analytic function into a sum of basic signals, called mono-components. Unlike the Fourier series decomposition, the AFD is based on an adaptive rational orthogonal system, hence it is better suited for analyzing non-stationary data. The most popular algorithm for the AFD decomposes any signal in such a way that the energy of the low-frequency components is maximized. Unfortunately, this results in poor energy compaction of high-frequency components. In this paper, we develop a novel algorithm for the AFD. The key idea is to maximize the energy of any components no matter how big or small the corresponding frequencies are. A comparative evaluation was conducted of the signal reconstruction efficiency of the proposed approach and several conventional algorithms by using speech recordings. The experimental results show that with the new algorithm, it is possible to get a better performance in terms of the reconstruction quality and energy compaction property.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"62 1","pages":"2348-2352"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86369905","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
Multipitch tracking in music signals using Echo State Networks 回声状态网络在音乐信号中的多音高跟踪
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287638
P. Steiner, Simon Stone, P. Birkholz, A. Jalalvand
{"title":"Multipitch tracking in music signals using Echo State Networks","authors":"P. Steiner, Simon Stone, P. Birkholz, A. Jalalvand","doi":"10.23919/Eusipco47968.2020.9287638","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287638","url":null,"abstract":"Currently, convolutional neural networks (CNNs) define the state of the art for multipitch tracking in music signals. Echo State Networks (ESNs), a recently introduced recurrent neural network architecture, achieved similar results as CNNs for various tasks, such as phoneme or digit recognition. However, they have not yet received much attention in the community of Music Information Retrieval. The core of ESNs is a group of unordered, randomly connected neurons, i.e., the reservoir, by which the low-dimensional input space is non-linearly transformed into a high-dimensional feature space. Because only the weights of the connections between the reservoir and the output are trained using linear regression, ESNs are easier to train than deep neural networks. This paper presents a first exploration of ESNs for the challenging task of multipitch tracking in music signals. The best results presented in this paper were achieved with a bidirectional two-layer ESN with 20 000 neurons in each layer. Although the final F-score of 0.7198 still falls below the state of the art (0.7370), the proposed ESN-based approach serves as a baseline for further investigations of ESNs in audio signal processing in the future.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"2012 1","pages":"126-130"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86352897","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: 13
User Activity And Data Detection For MIMO Uplink C-RAN Using Bayesian Learning 基于贝叶斯学习的MIMO上行链路C-RAN用户活动和数据检测
2020 28th European Signal Processing Conference (EUSIPCO) Pub Date : 2021-01-24 DOI: 10.23919/Eusipco47968.2020.9287867
Anupama Rajoriya, Vidushi Katiyar, Rohit Budhiraja
{"title":"User Activity And Data Detection For MIMO Uplink C-RAN Using Bayesian Learning","authors":"Anupama Rajoriya, Vidushi Katiyar, Rohit Budhiraja","doi":"10.23919/Eusipco47968.2020.9287867","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287867","url":null,"abstract":"We investigate user activity and data detection problem in a multiple-input multiple-output uplink cloud-radio access network, where the data matrix over a time-frame has overlapped burst sparsity due to sporadic user activity. We exploit this sparsity to recover data by proposing a weighted prior-sparse Bayesian learning algorithm. The proposed algorithm, due to carefully selected prior, captures not only the overlapped burst sparsity across time but also the block sparsity due to multi-user antennas. We also derive hyperparameter updates, and estimate the weight parameters using the support estimated via index-wise log-likelihood ratio test. We numerically demonstrate that the proposed algorithm has much lower bit error rate than the state-of-the-art competing algorithms.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"21 1","pages":"1742-1746"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89220420","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
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