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

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Recovery guarantees for mixed norm ℓp1, p2 block sparse representations 混合范数p1, p2块稀疏表示的恢复保证
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760274
F. Afdideh, R. Phlypo, C. Jutten
{"title":"Recovery guarantees for mixed norm ℓp1, p2 block sparse representations","authors":"F. Afdideh, R. Phlypo, C. Jutten","doi":"10.1109/EUSIPCO.2016.7760274","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760274","url":null,"abstract":"In this work, we propose theoretical and algorithmic-independent recovery conditions which guarantee the uniqueness of block sparse recovery in general dictionaries through a general mixed norm optimization problem. These conditions are derived using the proposed block uncertainty principles and block null space property, based on some newly defined characterizations of block spark, and (p, p)-block mutual incoherence. We show that there is improvement in the recovery condition when exploiting the block structure of the representation. In addition, the proposed recovery condition extends the similar results for block sparse setting by generalizing the criterion for determining the active blocks, generalizing the block sparse recovery condition, and relaxing some constraints on blocks such as linear independency of the columns.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"2 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":"116888962","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
Estimation of the spatial information in Gaussian model based audio source separation using weighted spectral bases 基于高斯模型的音频源分离中空间信息的加权谱基估计
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760436
M. Fakhry, P. Svaizer, M. Omologo
{"title":"Estimation of the spatial information in Gaussian model based audio source separation using weighted spectral bases","authors":"M. Fakhry, P. Svaizer, M. Omologo","doi":"10.1109/EUSIPCO.2016.7760436","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760436","url":null,"abstract":"In Gaussian model based audio source separation, source spatial images are modeled by Gaussian distributions. The covariance matrices of the distributions are represented by source variances and spatial covariance matrices. Accordingly, the likelihood of observed mixtures of independent source signals is parametrized by the variances and the covariance matrices. The separation is performed by estimating the parameters and applying multichannel Wiener filtering. Assuming that spectral basis matrices trained on source power spectra are available, this work proposes a method to estimate the parameters by maximizing the likelihood using Expectation-Maximization. In terms of normalization, the variances are estimated applying singular value decomposition. Furthermore, by building weighted matrices from vectors of the trained matrices, semi-supervised nonnegative matrix factorization is applied to estimate the spatial covariance matrices. The experimental results prove the efficiency of the proposed algorithm in reverberant environments.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"166 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120869980","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
Low complexity FRI based sampling scheme for UWB channel estimation 基于低复杂度FRI的UWB信道估计采样方案
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760330
Tina Yaacoub, Roua Youssef, E. Radoi, G. Burel
{"title":"Low complexity FRI based sampling scheme for UWB channel estimation","authors":"Tina Yaacoub, Roua Youssef, E. Radoi, G. Burel","doi":"10.1109/EUSIPCO.2016.7760330","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760330","url":null,"abstract":"In this paper, we propose a low complexity multichannel scheme for Ultra Wideband (UWB) channel impulse response estimation. It is mainly based on the finite rate of innovation (FRI) characteristic of UWB channel impulse response, which allows for a sampling frequency much lower than the Nyquist limit. Since the UWB channel is rich in multipaths, the number of samples required results in an unrealistic number of processing channels. Our approach removes this drawback at the price of a moderate increase of the number of pilot pulses. Compared to other schemes presented in the literature, the one proposed in this paper allows reducing the number of processing channels to values appropriate for practical implementation. Moreover, the same approach is used to further reduce the sampling frequency at each channel. The effectiveness of the proposed approach is demonstrated for IEEE 802.15.3a UWB channel estimation in a coherent reception framework.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"4 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121008440","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 simple set-membership affine projection algorithm for sparse system modeling 稀疏系统建模中一种简单的集隶属度仿射投影算法
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760558
Hamed Yazdanpanah, P. Diniz, Markus V. S. Lima
{"title":"A simple set-membership affine projection algorithm for sparse system modeling","authors":"Hamed Yazdanpanah, P. Diniz, Markus V. S. Lima","doi":"10.1109/EUSIPCO.2016.7760558","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760558","url":null,"abstract":"In this paper, we derive two algorithms, namely the Simple Set-Membership Affine Projection (S-SM-AP) and the improved S-SM-AP (IS-SM-AP), in order to exploit the sparsity of an unknown system while focusing on having low computational complexity. To achieve this goal, the proposed algorithms apply a discard function on the weight vector to disregard the coefficients close to zero during the update process. In addition, the IS-SM-AP algorithm reduces the overall number of computations required by the adaptive filter even further by replacing small coefficients with zero. Simulation results show similar performance when comparing the proposed algorithm with some existing state-of-the-art sparsity-aware algorithms while the proposed algorithms require lower computational complexity.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"58 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":"124900606","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: 30
On scatter matrix estimation in the presence of unknown extra parameters: Mismatched scenario 存在未知额外参数的散点矩阵估计:不匹配情况
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760635
S. Fortunati, F. Gini, M. Greco
{"title":"On scatter matrix estimation in the presence of unknown extra parameters: Mismatched scenario","authors":"S. Fortunati, F. Gini, M. Greco","doi":"10.1109/EUSIPCO.2016.7760635","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760635","url":null,"abstract":"In this paper, a Constrained Mismatched Maximum Likelihood (CMML) estimator for the joint estimation of the scatter matrix and the power of Complex Elliptically Symmetric (CES) distributed vectors is derived under misspecified data models. Specifically, this estimator is obtained by assuming a Normal model while the data are sampled from a complex t-distribution. The convergence point of such CMML estimator is investigated and its Mean Square Error (MSE) compared with the Constrained Misspecified Cramér-Rao Bound (CMCRB).","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"13 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":"125360073","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
High quality voice conversion by post-filtering the outputs of Gaussian processes 高质量的语音转换后滤波的输出高斯过程
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760371
N. Xu, Xiao Yao, A. Jiang, Xiaofeng Liu, J. Bao
{"title":"High quality voice conversion by post-filtering the outputs of Gaussian processes","authors":"N. Xu, Xiao Yao, A. Jiang, Xiaofeng Liu, J. Bao","doi":"10.1109/EUSIPCO.2016.7760371","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760371","url":null,"abstract":"Voice conversion is a technique that aims to transform the individuality of source speech so as to mimic that of target speech while keeping the message unaltered, where the Gaussian mixture model based methods are most commonly used. However, these methods suffer from over-smoothing and over-fitting problems. In our previous work, we proposed to use Gaussian processes to alleviate over-fitting. Despite its effectiveness, this method will inevitably lead to over-smoothing due to choosing the mean of predictive distribution of Gaussian processes as optimal estimation. Thus, in this paper we focus on addressing the over-smoothing problem by post-filtering the outputs of the standard Gaussian processes, resulting in more dynamics in the converted feature parameters. Experiments have confirmed the validity of the proposed method both objectively and subjectively.","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":"122482098","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
Compressed sensing in MRI with a Markov random field prior for spatial clustering of subband coefficients 基于马尔可夫随机场的MRI压缩感知子带系数空间聚类
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760311
Marko Neven Panić, J. Aelterman, V. Crnojevic, A. Pižurica
{"title":"Compressed sensing in MRI with a Markov random field prior for spatial clustering of subband coefficients","authors":"Marko Neven Panić, J. Aelterman, V. Crnojevic, A. Pižurica","doi":"10.1109/EUSIPCO.2016.7760311","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760311","url":null,"abstract":"Recent work in compressed sensing of magnetic resonance images (CS-MRI) concentrates on encoding structured sparsity in acquisition or in the reconstruction stages. Subband coefficients of typical images obey a certain structure, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Approaches using wavelet tree-sparsity have already demonstrated excellent performance in MRI. However, the use of statistical models for spatial clustering of the subband coefficients has not been studied well in CS-MRI yet, although the potentials of such an approach have been indicated. In this paper, we design a practical reconstruction algorithm as a variant of the proximal splitting methods, making use of a Markov Random Field prior model for spatial clustering of subband coefficients. The results for different undersampling patterns demonstrate an improved reconstruction performance compared to both standard CS-MRI methods and methods based on wavelet tree sparsity.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"195 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":"122872147","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: 7
Reversible watermarking based on complementary predictors and context embedding 基于互补预测和上下文嵌入的可逆水印
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760434
Ioan-Catalin Dragoi, D. Coltuc
{"title":"Reversible watermarking based on complementary predictors and context embedding","authors":"Ioan-Catalin Dragoi, D. Coltuc","doi":"10.1109/EUSIPCO.2016.7760434","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760434","url":null,"abstract":"Two complementary bounds of the predicted pixel are defined and used to compute two estimates of the prediction error. The more suitable value for reversible watermarking among the two estimates is selected for data insertion. A reversible watermarking scheme based on context embedding ensures the detection of the selected value, without the need for any additional information. The scheme is general and works regardless the particular predictor. The proposed scheme is of interest for embedding bit-rates of less than 0.5 bpp. Interesting results are reported for the case of pairwise embedding reversible watermarking. The proposed scheme compares very well with the most efficient schemes published so far.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"43 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":"117002699","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
Efficient relative transfer function estimation framework in the spherical harmonics domain 球谐波域中有效的相对传递函数估计框架
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760530
Yoav Biderman, B. Rafaely, S. Gannot, S. Doclo
{"title":"Efficient relative transfer function estimation framework in the spherical harmonics domain","authors":"Yoav Biderman, B. Rafaely, S. Gannot, S. Doclo","doi":"10.1109/EUSIPCO.2016.7760530","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760530","url":null,"abstract":"In acoustic conditions with reverberation and coherent sources, various spatial filtering techniques, such as the linearly constrained minimum variance (LCMV) beamformer, require accurate estimates of the relative transfer functions (RTFs) between the sensors with respect to the desired speech source. However, the time-domain support of these RTFs may affect the estimation accuracy in several ways. First, short RTFs justify the multiplicative transfer function (MTF) assumption when the length of the signal time frames is limited. Second, they require fewer parameters to be estimated, hence reducing the effect of noise and model errors. In this paper, a spherical microphone array based framework for RTF estimation is presented, where the signals are transformed to the spherical harmonics (SH)-domain. The RTF time-domain supports are studied under different acoustic conditions, showing that SH-domain RTFs are shorter compared to conventional space-domain RTFs.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"90 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":"128598168","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
Frequency-domain blind speech separation using incomplete de-mixing transform 基于不完全解混变换的频域盲语音分离
2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI: 10.1109/EUSIPCO.2016.7760531
Zbyněk Koldovský, F. Nesta, P. Tichavský, Nobutaka Ono
{"title":"Frequency-domain blind speech separation using incomplete de-mixing transform","authors":"Zbyněk Koldovský, F. Nesta, P. Tichavský, Nobutaka Ono","doi":"10.1109/EUSIPCO.2016.7760531","DOIUrl":"https://doi.org/10.1109/EUSIPCO.2016.7760531","url":null,"abstract":"We propose a novel solution to the blind speech separation problem where the de-mixing transform is estimated only within selected frequency bins. This solution is based on Independent Vector Analysis applied to a subset of instantaneous mixtures, one per selected frequency bin. Next, two approaches are proposed to complete the transform: one based on null beamforming, and the other based on convex programming. In subsequent experiments, we compare combinations of both methods and evaluate their ability to retrieve the whole de-mixing transform. Depending on the number of selected frequencies and the sparsity of room impulse responses, the methods show improvements in terms of computational complexity as well as in terms of separation accuracy.","PeriodicalId":127068,"journal":{"name":"2016 24th European Signal Processing Conference (EUSIPCO)","volume":"227 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":"128618700","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
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