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2016 24th European Signal Processing Conference (EUSIPCO)最新文献

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Extension of Generalized Hammerstein model to non-polynomial inputs 广义Hammerstein模型在非多项式输入上的推广
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760202
A. Novák, L. Simon, P. Lotton
{"title":"Extension of Generalized Hammerstein model to non-polynomial inputs","authors":"A. Novák, L. Simon, P. Lotton","doi":"10.1109/EUSIPCO.2016.7760202","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760202","url":null,"abstract":"The Generalized Hammerstein model has been successfully used during last few years in many physical applications to describe the behavior of a nonlinear system under test. The main advantage of such a nonlinear model is its capability to model efficiently nonlinear systems while keeping the computational cost low. On the other hand, this model can not predict complicated nonlinear behaviors such as hysteretic one. In this paper, we propose an extension of the Generalized Hammerstein model to a model with non polynomial nonlinear inputs that allows modeling more complicated nonlinear systems. A simulation provided in this paper shows a good agreement between the model and the hysteretic nonlinear system under test.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130002271","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
High rate quantization analysis for a class of finite rate of innovation signals 一类有限创新率信号的高速率量化分析
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760283
Ajinkya Jayawant, Animesh Kumar
{"title":"High rate quantization analysis for a class of finite rate of innovation signals","authors":"Ajinkya Jayawant, Animesh Kumar","doi":"10.1109/EUSIPCO.2016.7760283","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760283","url":null,"abstract":"Acquisition and perfect reconstruction of finite rate of innovation (FRI) signals was proposed first by Vetterli, Marziliano, and Blu [1]. To the best of our knowledge, the stability of their reconstruction procedure in the presence of scalar quantizers has not been addressed in the literature. For periodic stream of Dirac FRI signal, which is an important subclass of FRI signals, the stability of reconstruction when quantization is introduced on acquired samples is analyzed in this work. It is shown that the parameters of stream of Diracs can be obtained with error O(ε), where ε is the per sample quantization error. This result holds in the high-rate quantization regime when ε is sufficiently small.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"54 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134427451","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
Self-adaptive ground calibration in binocular surveillance system 双目监视系统自适应地面标定
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760455
Diwen Liu, Ling Cai, Yuming Zhao, Fuqiao Hu
{"title":"Self-adaptive ground calibration in binocular surveillance system","authors":"Diwen Liu, Ling Cai, Yuming Zhao, Fuqiao Hu","doi":"10.1109/EUSIPCO.2016.7760455","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760455","url":null,"abstract":"Object detection and tracking have always been crucial and challenging topics in computer vision. Compared with monocular vision systems, binocular vision systems (BVSs) have the advantage of dealing with illumination variation, shadow interference, and severe occlusion. Usually, the BVS constructs the world coordinates system by manually calibrating the ground plane. However, the camera vibrations decreases the calibration precision and weakens the system performance. To automatically correct and update the parameters of ground plane, we introduce Linear Discriminant Analysis (LDA) method to analyze the results of object localization and include the feedback in the surveillance system, in this way, a close loop system that greatly improves the accuracy and stability of surveillance system is constructed. Experimental results demonstrate that our approach works well in BVS for video surveillance.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134379989","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
3D point cloud segmentation oriented to the analysis of interactions 面向交互分析的三维点云分割
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760379
Xiao Lin, J. Casas, M. Pardàs
{"title":"3D point cloud segmentation oriented to the analysis of interactions","authors":"Xiao Lin, J. Casas, M. Pardàs","doi":"10.1109/EUSIPCO.2016.7760379","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760379","url":null,"abstract":"Given the widespread availability of point cloud data from consumer depth sensors, 3D point cloud segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis. It benefits from the richer information contained in real world 3D data compared to 2D images. This also implies that the classical color segmentation challenges have shifted to RGBD data, and new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. Meanwhile, the lack of 3D point cloud ground truth labeling also limits the development and comparison among methods in 3D point cloud segmentation. In this paper, we present two contributions: a novel graph based point cloud segmentation method for RGBD stream data with interacting objects and a new ground truth labeling for a previously published data set [1]. This data set focuses on interaction (merge and split between `object' point clouds), which differentiates itself from the few existing labeled RGBD data sets which are more oriented to Simultaneous Localization And Mapping (SLAM) tasks. The proposed point cloud segmentation method is evaluated with the 3D point cloud ground truth labeling. Experiments show the promising result of our approach.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131568995","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
Towards dynamic classification completeness in Twitter 迈向Twitter的动态分类完备性
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760418
Dimitris Milioris
{"title":"Towards dynamic classification completeness in Twitter","authors":"Dimitris Milioris","doi":"10.1109/EUSIPCO.2016.7760418","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760418","url":null,"abstract":"In this paper we study the application of Matrix Completion in topic detection and classification in Twitter. The proposed method first employs Joint Complexity to perform topic detection based on score matrices. Based on the spatial correlation of tweets and the spatial characteristics of the score matrices, we apply a novel framework which extends the Matrix Completion to build dynamically complete matrices from a small number of random sample Joint Complexity scores. The experimental evaluation with real data from Twitter presents the topic detection accuracy based on complete reconstructed matrices, and thus reducing the exhaustive computation of Joint Complexity scores.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131646565","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
Improving narrowband DOA estimation of sound sources using the complex Watson distribution 利用复沃森分布改进声源窄带DOA估计
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760492
Anastasios Alexandridis, A. Mouchtaris
{"title":"Improving narrowband DOA estimation of sound sources using the complex Watson distribution","authors":"Anastasios Alexandridis, A. Mouchtaris","doi":"10.1109/EUSIPCO.2016.7760492","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760492","url":null,"abstract":"Narrowband direction-of-arrival (DOA) estimates for each time-frequency (TF) point offer a parametric spatial modeling of the acoustic environment which is very commonly used in many applications, such as source separation, dereverberation, and spatial audio. However, irrespective of the narrowband DOA estimation method used, many TF-points suffer from erroneous estimates due to noise and reverberation. We propose a novel technique to yield more accurate DOA estimates in the TF-domain, through statistical modeling of each TF-point with a complex Watson distribution. Then, instead of using the microphone array signals at a given TF-point to estimate the DOA, the maximum likelihood estimate of the mode vector of the distribution is used as input to the DOA estimation method. This approach results in more accurate DOA estimates and thus more accurate modeling of the acoustic environment, while it can be used with any narrowband DOA estimation method and microphone array geometry.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"2008 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127557629","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
Kronecker covariance sketching for spatial-temporal data 时空数据的Kronecker协方差草图
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760261
Yuejie Chi
{"title":"Kronecker covariance sketching for spatial-temporal data","authors":"Yuejie Chi","doi":"10.1109/EUSIPCO.2016.7760261","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760261","url":null,"abstract":"Covariance sketching has been recently introduced as an effective strategy to reduce the data dimensionality without sacrificing the ability to reconstruct second-order statistics of the data. In this paper, we propose a novel covariance sketching scheme with reduced complexity for spatial-temporal data, whose covariance matrices satisfy the Kronecker product expansion model recently introduced by Tsiligkaridis and Hero. Our scheme is based on quadratic sampling that only requires magnitude measurements, hence is appealing for applications when phase information is difficult to obtain, such as wideband spectrum sensing and optical imaging. We propose to estimate the covariance matrix based on convex relaxation when the separation rank is small, and when the temporal covariance is additionally Toeplitz structured. Numerical examples are provided to demonstrate the effectiveness of the proposed scheme.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132610272","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: 5
Total-activation regularized deconvolution of resting-state fMRI leads to reproducible networks with spatial overlap 静息状态fMRI的全激活正则化反褶积导致具有空间重叠的可重复网络
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760250
F. I. Karahanoğlu, D. Ville
{"title":"Total-activation regularized deconvolution of resting-state fMRI leads to reproducible networks with spatial overlap","authors":"F. I. Karahanoğlu, D. Ville","doi":"10.1109/EUSIPCO.2016.7760250","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760250","url":null,"abstract":"Spontaneous activations in resting-state fMRI have been shown to corroborate recurrent intrinsic functional networks. Recent studies have explored integration of brain function in terms of spatially overlapping networks. We have proposed a method to recover not only spatially but also temporally overlapping networks, which we named innovation-driven co-activation patterns (iCAPs). These networks are driven by the sparse innovation signals recovered from Total Activation (TA), a spatiotemporal regularization framework for fMRI deconvolution. The fMRI data is processed with TA, which uses the inverse of the hemodynamic response function - as a linear differential operator - combined with the derivative in the regularization with ℓ1-norm. As a result, sparse innovation signals are reconstructed as the deconvolved fMRI time series. Temporal clustering of innovation signals lead to iCAPs. In this work, we investigate the reproducible iCAPs in individuals with relapsing-remitting multiple sclerosis and healthy volunteers.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130931879","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
Gigabit DSL: A deep-LMS approach 千兆DSL:一种深度lms方法
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760258
A. Zanko, I. Bergel, Amir Leshem
{"title":"Gigabit DSL: A deep-LMS approach","authors":"A. Zanko, I. Bergel, Amir Leshem","doi":"10.1109/EUSIPCO.2016.7760258","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760258","url":null,"abstract":"In this paper we present the Deep-LMS, a novel algorithm for crosstalk cancellation in DSL. The Deep-LMS crosstalk canceler uses an adaptive non-diagonal preprocessing matrix prior to a conventional LMS crosstalk canceler. The role of the preprocessing matrix is to speed-up the convergence of the conventional LMS crosstalk canceler and hence speed-up the convergence of the overall system. The update of the preprocessing matrix is inspired by deep neural networks. However, since all the operations in the Deep-LMS algorithm are linear, we are capable of providing an exact convergence speed analysis. The Deep-LMS is important for crosstalk cancellation in the novel G.fast standard, where traditional LMS converges very slowly due to the large bandwidth. Simulation results support our analysis and show significant reduction in convergence time compared to existing LMS variants.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132834650","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: 4
Multi-class learning algorithm for deep neural network-based statistical parametric speech synthesis 基于深度神经网络的统计参数语音合成多类学习算法
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760589
Eunwoo Song, Hong-Goo Kang
{"title":"Multi-class learning algorithm for deep neural network-based statistical parametric speech synthesis","authors":"Eunwoo Song, Hong-Goo Kang","doi":"10.1109/EUSIPCO.2016.7760589","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760589","url":null,"abstract":"This paper proposes a multi-class learning (MCL) algorithm for a deep neural network (DNN)-based statistical parametric speech synthesis (SPSS) system. Although the DNN-based SPSS system improves the modeling accuracy of statistical parameters, its synthesized speech is often muffled because the training process only considers the global characteristics of the entire set of training data, but does not explicitly consider any local variations. We introduce a DNN-based context clustering algorithm that implicitly divides the training data into several classes, and train them via a shared hidden layer-based MCL algorithm. Since the proposed MCL method efficiently models both the universal and class-dependent characteristics of various phonetic information, it not only avoids the model over-fitting problem but also reduces the over-smoothing effect. Objective and subjective test results also verify that the proposed algorithm performs much better than the conventional method.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132656640","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
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