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2011 19th European Signal Processing Conference最新文献

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Empirical mode decomposition for joint denoising and dereverberation 联合去噪和去噪的经验模态分解
2011 19th European Signal Processing Conference Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900818
A. Ghalib, T. Jan
{"title":"Empirical mode decomposition for joint denoising and dereverberation","authors":"A. Ghalib, T. Jan","doi":"10.1109/ICDSP.2014.6900818","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900818","url":null,"abstract":"We propose a novel algorithm for the enhancement of noisy reverberant speech using empirical-mode-decomposition (EMD) based subband processing. The proposed algorithm is a one-microphone multistage algorithm. In the first step, noisy reverberant speech is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs) via an EMD algorithm. Denoising is then applied to selected high frequency IMFs using EMD-based minimum means-quared error (MMSE) filter, followed by spectral subtraction of the resulting denoised high-frequency IMFs and low-frequency IMFs. Finally, the enhanced speech signal is reconstructed from the processed IMFs. The method was motivated by our observation that the noise and reverberations are disproportionally distributed across the IMF components. Therefore, different levels of suppression can be applied to the additive noise and reverberation in each IMF. This leads to an improved enhancement performance as shown in comparison to a related recent approach, based on the measurements by the signal-to-noise ratio (SNR).","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128618821","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
Bayesian partial out-of-focus blur removal with parameter estimation 基于参数估计的贝叶斯局部失焦模糊去除
2011 19th European Signal Processing Conference Pub Date : 2011-12-01 DOI: 10.5281/ZENODO.42728
Bruno Amizic, R. Molina, A. Katsaggelos
{"title":"Bayesian partial out-of-focus blur removal with parameter estimation","authors":"Bruno Amizic, R. Molina, A. Katsaggelos","doi":"10.5281/ZENODO.42728","DOIUrl":"https://doi.org/10.5281/ZENODO.42728","url":null,"abstract":"In this paper we propose a novel partial out-of-focus blur removal method developed within the Bayesian framework. We concentrate on the removal of background out-of-focus blurs that are present in the images in which there is a strong interest to keep the foreground in sharp focus. However, often there is a desire to recover background details out of such partially blurred image. In this work, a non-convex lp-norm prior with 0 <; p <; 1 is used as the background and foreground image prior and a total variation (TV) based prior is utilized for both the background blur and the occlusion mask, that is, the mask determining the pixels belonging to the foreground. In order to model transparent foregrounds, the values in the occlusion mask are assumed to belong to the closed interval [0,1]. The proposed method is derived by utilizing bounds on the priors for the background and foreground image, the background blur and the occlusion mask using the majorization-minimization principle. Maximum a posteriori Bayesian inference is performed and as a result, the background and foreground image, the background blur, the occlusion mask and the model parameters are simultaneously estimated. Experimental results are presented to demonstrate the advantage of the proposed method over the existing ones.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115412394","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
Compressed sensing for volterra and polynomial regression models volterra压缩感知与多项式回归模型
2011 19th European Signal Processing Conference Pub Date : 2011-12-01 DOI: 10.5281/ZENODO.42715
V. Kekatos, G. Giannakis
{"title":"Compressed sensing for volterra and polynomial regression models","authors":"V. Kekatos, G. Giannakis","doi":"10.5281/ZENODO.42715","DOIUrl":"https://doi.org/10.5281/ZENODO.42715","url":null,"abstract":"Volterra filtering and polynomial regression are two widely utilized tools for nonlinear system modeling and inference. They are both critically challenged by the curse of dimensionality, which is typically alleviated via kernel regression. However, exciting diverse applications ranging from neuroscience to genome-wide association (GWA) analysis call for parsimonious polynomial expansions of critical interpretative value. Unfortunately, kernel regression cannot yield sparsity in the primal domain, where compressed sampling approaches can offer a viable alternative. Following the compressed sampling principle, a sparse polynomial expansion can be recovered by far fewer measurements compared to the least squares (LS)-based approaches. But how many measurements are sufficient for a given level of sparsity? This paper is the first attempt to answer this question by analyzing the restricted isometry properties for commonly met polynomial regression settings. Additionally, the merits of compressed sampling approaches to polynomial modeling are corroborated on synthetic and real data for quantitative genotype-phenotype analysis.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117075444","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
Robust conjoint analysis by controlling outlier sparsity 控制离群稀疏度的鲁棒联合分析
2011 19th European Signal Processing Conference Pub Date : 2011-12-01 DOI: 10.5281/ZENODO.42366
G. Mateos, G. Giannakis
{"title":"Robust conjoint analysis by controlling outlier sparsity","authors":"G. Mateos, G. Giannakis","doi":"10.5281/ZENODO.42366","DOIUrl":"https://doi.org/10.5281/ZENODO.42366","url":null,"abstract":"Preference measurement (PM) has a long history in marketing, healthcare, and the biobehavioral sciences, where conjoint analysis is commonly used. The goal of PM is to learn the utility function of an individual or a group of individuals from expressed preference data (buying patterns, surveys, ratings), possibly contaminated with outliers. For metric conjoint data, a robust partworth estimator is developed on the basis of a neat connection between ℓ0-(pseudo)norm-regularized regression, and the least-trimmed squared estimator. This connection suggests efficient solvers based on convex relaxation, which lead naturally to a family of robust estimators subsuming Huber's optimal M-class. Outliers are identified by tuning a regularization parameter, which amounts to controlling the sparsity of an outlier vector along the entire robustification path of least-absolute shrinkage and selection operator solutions. For choice-based conjoint analysis, a novel classifier is developed that is capable of attaining desirable tradeoffs between model fit and complexity, while at the same time controlling robustness and revealing the outliers present. Variants accounting for nonlinear utilities and consumer heterogeneity are also investigated.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129520586","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: 8
Nonlinear adaptive filtering techniques with multiple kernels 多核非线性自适应滤波技术
2011 19th European Signal Processing Conference Pub Date : 2011-12-01 DOI: 10.5281/ZENODO.42304
M. Yukawa
{"title":"Nonlinear adaptive filtering techniques with multiple kernels","authors":"M. Yukawa","doi":"10.5281/ZENODO.42304","DOIUrl":"https://doi.org/10.5281/ZENODO.42304","url":null,"abstract":"In this paper, we propose a novel approach using multiple kernels to nonlinear adaptive filtering problems. We present two types of multi-kernel adaptive filtering algorithms, both of which are based on the kernel normalized least mean square (KNLMS) algorithm (Richard et al., 2009). One is a simple generalization of KNLMS, adopting the coherence criterion for dictionary selection. The other is derived by applying the adaptive proximal forward-backward splitting method to a certain squared distance function penalized by a weighted block ℓ1 norm. The latter algorithm operates the weighted block soft-thresholding which encourages the sparsity of dictionary at the block level. Numerical examples demonstrate the efficacy of the proposed approach.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115072651","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
Radar code design with a Peak to average power Ratio constraint: A randomized approximate approach 具有峰值与平均功率比约束的雷达代码设计:随机近似方法
2011 19th European Signal Processing Conference Pub Date : 2011-12-01 DOI: 10.5281/ZENODO.42377
A. Maio, Yongwei Huang, M. Piezzo, Shuzhong Zhang, A. Farina
{"title":"Radar code design with a Peak to average power Ratio constraint: A randomized approximate approach","authors":"A. Maio, Yongwei Huang, M. Piezzo, Shuzhong Zhang, A. Farina","doi":"10.5281/ZENODO.42377","DOIUrl":"https://doi.org/10.5281/ZENODO.42377","url":null,"abstract":"This paper considers the problem of radar waveform design in the presence of colored Gaussian disturbance under a Peak to Average power Ratio (PAR) and an energy constraint. Firstly, we focus on the selection of the radar signal optimizing the Signal to Noise Power Ratio (SNR) for a given target Doppler frequency (Algorithm 1). Then, we devise its phase quantized version (Algorithm 2), which forces the waveform phase to lie within a finite alphabet. Both the problems are formulated in terms of NP-hard non-convex quadratic optimization programs; in order to solve them, we resort to Semidefinite Programming (SDP) relaxation and randomization techniques, providing provable-quality sub-optimal solutions with a polynomial time computational complexity. Finally, we analyze the performance in terms of detection capability and robustness with respect to Doppler shifts.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114574887","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
Sparsity-aware adaptive filtering based on a Douglas-Rachford splitting 基于Douglas-Rachford分裂的稀疏感知自适应滤波
2011 19th European Signal Processing Conference Pub Date : 2011-08-29 DOI: 10.5281/ZENODO.42526
I. Yamada, Silvia Gandy, M. Yamagishi
{"title":"Sparsity-aware adaptive filtering based on a Douglas-Rachford splitting","authors":"I. Yamada, Silvia Gandy, M. Yamagishi","doi":"10.5281/ZENODO.42526","DOIUrl":"https://doi.org/10.5281/ZENODO.42526","url":null,"abstract":"In this paper, we propose a novel online scheme for the sparse adaptive filtering problem. It is based on a formulation of the adaptive filtering problem as a minimization of the sum of (possibly nonsmooth) convex functions. Our proposed scheme is a time-varying extension of the so-called Douglas-Rachford splitting method. It covers many existing adaptive filtering algorithms as special cases. We show several examples of special choices of the cost functions that reproduce those existing algorithms. Our scheme achieves a monotone decrease of an upper bound of the distance to the solution set of the minimization under certain conditions. We applied a simple algorithm that falls under our scheme to a sparse echo cancellation problem where it shows excellent convergence performance.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116238686","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: 15
Efficient blind speech separation suitable for embedded devices 适用于嵌入式设备的高效盲语音分离
2011 19th European Signal Processing Conference Pub Date : 2011-08-29 DOI: 10.5281/ZENODO.42625
Kazunobu Kondo, Yu Takahashi, S. Hashimoto, H. Saruwatari, Takanori Nishino, K. Takeda
{"title":"Efficient blind speech separation suitable for embedded devices","authors":"Kazunobu Kondo, Yu Takahashi, S. Hashimoto, H. Saruwatari, Takanori Nishino, K. Takeda","doi":"10.5281/ZENODO.42625","DOIUrl":"https://doi.org/10.5281/ZENODO.42625","url":null,"abstract":"A blind speech separation method with a low computational complexity is proposed. This method consists of a combination of independent component analysis with frequency band selection, and a frame-wise spectral softmask method based on an inter-channel power ratio of tentative separated signals in the frequency domain. The softmask cancels the transfer function between sources and separated signals. A theoretical analysis is given. Performance and effectiveness are evaluated via source separation simulations and a computational estimate, and experimental results show the significantly improved performance of the proposed method. The segmental signal-to-noise ratio achieves 7 [dB] and 3 [dB], and the cepstral distortion achieves 1 [dB] and 2.5 [dB], in anechoic and reverberant conditions, respectively. Moreover, there can be a reduction of over 80% in computational complexity compared with unmodified FDICA.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117246332","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
Distributed Khatri-Rao space-time coding and decoding for cooperative networks 协作网络的分布式Khatri-Rao空时编码与解码
2011 19th European Signal Processing Conference Pub Date : 2011-08-29 DOI: 10.5281/ZENODO.42704
A. Kibangou, A. D. Almeida
{"title":"Distributed Khatri-Rao space-time coding and decoding for cooperative networks","authors":"A. Kibangou, A. D. Almeida","doi":"10.5281/ZENODO.42704","DOIUrl":"https://doi.org/10.5281/ZENODO.42704","url":null,"abstract":"Khatri-Rao space-time (KRST) coding is an efficient scheme proposed in the last decade for multi-antenna communication systems. The decoding process results on a tensor decomposition with interesting blind decodability properties. In this paper, we consider its extension to cooperative networks where each node has a single antenna. For this purpose, we propose a distributed KRST coding and decoding technique. The proposed distributed decoding scheme is based on average consensus embedded in an alternating least squares (ALS) algorithm. Unlike standard consensus algorithms where consensus is reached asymptotically, we derive closed form solutions allowing to reach the consensus in a finite number of iterations upper-bounded by the number of collaborating nodes. The performance of the proposed method is evaluated by means of simulations.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117284516","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
Compressed sensing with Shannon-Kotel'nikov mapping in the presence of noise 噪声存在下的Shannon-Kotel'nikov映射压缩感知
2011 19th European Signal Processing Conference Pub Date : 2011-08-29 DOI: 10.5281/ZENODO.42417
A. Saleh, W. Chan, F. Alajaji
{"title":"Compressed sensing with Shannon-Kotel'nikov mapping in the presence of noise","authors":"A. Saleh, W. Chan, F. Alajaji","doi":"10.5281/ZENODO.42417","DOIUrl":"https://doi.org/10.5281/ZENODO.42417","url":null,"abstract":"We propose a low delay/complexity sensor system based on the combination of Shannon-Kotel'nikov mapping and compressed sensing (CS). The proposed system uses a 1:2 nonlinear analog coder on the CS measurements in the presence of channel noise. It is shown that the purely-analog system, used in conjunction with either maximum a-posteriori or minimum mean square error decoding, outperforms the following reference systems in terms of signal-to-distortion ratio: 1) a conventional CS system that assumes noiseless transmission, and 2) a CS-based system which accounts for channel noise during signal reconstruction. The proposed system is also shown to be advantageous in requiring fewer sensors than the reference systems.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129560513","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
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