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Optimized Speech Dereverberation From Probabilistic Perspective for Time Varying Acoustic Transfer Function

IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2013年第7期21卷 1369-1380页

作者： Togami, Masahito Kawaguchi, Yohei Takeda, Ryu Obuchi, Yasunari Nukaga, Nobuo Hitachi Ltd Cent Res Lab Tokyo 1858601 Japan

A dereverberation technique has been developed that optimally combines multichannel inverse filtering (MIF), beam-forming (BF), and non-linear reverberation suppression (NRS). It is robust against acoustic transfer function (ATF) fluctuations and creates less distortion than the NRS alone. The three components are optimally combined from a probabilistic perspective using a unified likelihood function incorporating two probabilistic models. A multichannel probabilistic source model based on a recently proposed local Gaussian model (LGM) provides robustness against ATF fluctuations of the early reflection. A probabilistic reverberant transfer function model (PRTFM) provides robustness against ATF fluctuations of the late reverberation. The MIF and multichannel under-determined source separation (MUSS) are optimized in an iterative manner. The MIF is designed to reduce the time-invariant part of the late reverberation by using optimal time-weighting with reference to the PRTFM and the LGM. The MUSS separates the dereverberated speech signal and the residual reverberation after the MIF, which can be interpreted as an optimized combination of the BF and the NRS. The parameters of the PRTFM and the LGM are optimized based on the MUSS output. Experimental results show that the proposed method is robust against the ATF fluctuations under both single and multiple source conditions.

关键词： Dereverberation expectation-maximization algorithm local Gaussian modeling multichannel filtering time-varying acoustic transfer function

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Practical approaches to principal component analysis for simultaneously dealing with missing and censored elements in chemical data

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ANALYTICA CHIMICA ACTA 2013年 796卷 27-37页

作者： Stanimirova, I. Silesian Univ Inst Chem Dept Theoret Chem PL-40006 Katowice Poland

Multivariate chemical data often contain elements that are missing completely at random and the so-called left-censored elements whose values are only known to be below a definite threshold value (reporting limit). In the last several years, attention has been paid to developing methods for dealing with data containing missing elements and those that can handle data with missing elements and outliers. However, processing data with both missing and left-censored elements is still an ongoing problem. The aim of this work was to investigate which method is most suitable for handling left-censored and missing completely at random elements that are present simultaneously in chemical data by using a comparison of the generalized nonlinear iterative partial least squares (NIPALS1) algorithm that has been recently proposed, methods that include uncertainty information like maximum likelihood principal component analysis, MLPCA2, and replacement methods. The results of the Monte Carlo simulation study for artificial and real data sets showed that substitution with half of the reporting limit can be used when the percentage of left-censored elements per variable is up to 30-40%. The generalized NIPALS algorithm is generally recommended for a large percentage of left-censored elements per variable and particularly when a large number of variables are censored. The expectation-maximization approach applied to data with censored elements substituted with half of the reporting limits can be a strategy for dealing with missing and left-censored elements in data, but if the converge criterion is not fulfilled, then the generalized NIPALS algorithm can be applied. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Left-censored data Generalized nonlinear iterative partial least squares algorithm Maximum likelihood principal component analysis expectation-maximization algorithm Positive matrix factorization

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Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2013年第3期227卷 538-551页

作者： Durango-Cohen, Elizabeth J. IIT Stuart Sch Business Chicago IL 60616 USA

Funding pressures have forced many not-for-profit organizations to reduce their reliance on mass-marketing efforts, e.g., pledge drives, and increase the volume and sophistication of their direct marketing activities. The efficiency of direct marketing, however, is linked to an organization's ability to target population segments effectively, which, in turn, has motivated the development of methodological approaches for market segmentation. In this paper, we use finite-mixture models as a framework to analyze member contributions at a public radio station in the Midwestern United States between 2001 and 2008. We exploit the methodology's flexibility, in a novel setting, to formulate segmentation models under different assumptions about the processes that generate the individual contribution sequences, i.e., longitudinal data. This differs from conventional models summarizing transactions with aggregate statistics, i.e., RFM data, that can introduce bias. We first consider generalized (Multivariate) Normal mixture models to obtain segmentations based on contribution frequencies and monetary value. To capture serial dependence in the contribution sequences, we present Markov Chain mixture models yielding segmentations based on behavioral patterns. We also derive instances of the expectation-maximization algorithm to estimate the associated parameters. Rather than modeling how segment size and membership evolve, we assume stable membership, and attribute manifest changes to the characteristics that define the segments. This, supports the identification of systematic, but unobserved, differences between individuals, and enables optimization of marketing policies for the ensuing segments. Analysis of the data from the public radio station shows significant heterogeneity in excess of that explained by residence location - a proxy for socioeconomic traits. In terms of managerial insights, we conclude that the contributions are largely driven by recommended membership level

关键词： Marketing Segmentation analysis Fundraising Not-for-profit organizations Finite-mixture models expectation-maximization algorithm

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Parameter estimation of nonlinear mixed-effects models using first-order conditional linearization and the EM algorithm

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JOURNAL OF APPLIED STATISTICS 2013年第2期40卷 252-265页

作者： Fu, Liyong Lei, Yuancai Sharma, Ram P. Tang, Shouzheng Chinese Acad Forestry Res Inst Forest Resource Informat Tech Beijing Peoples R China Norwegian Univ Life Sci Dept Ecol & Nat Resource Management As Norway

Nonlinear mixed-effects (NLME) models are flexible enough to handle repeated-measures data from various disciplines. In this article, we propose both maximum-likelihood and restricted maximum-likelihood estimations of NLME models using first-order conditional expansion (FOCE) and the expectationmaximization (EM) algorithm. The FOCE-EM algorithm implemented in the ForStat procedure SNLME is compared with the Lindstrom and Bates (LB) algorithm implemented in both the SAS macro NLINMIX and the S-Plus/R function nlme in terms of computational efficiency and statistical properties. Two realworld data sets an orange tree data set and a Chinese fir (Cunninghamia lanceolata) data set, and a simulated data set were used for evaluation. FOCE-EM converged for all mixed models derived from the base model in the two realworld cases, while LB did not, especially for the models in which random effects are simultaneously considered in several parameters to account for between-subject variation. However, both algorithms had identical estimated parameters and fit statistics for the converged models. We therefore recommend using FOCE-EM in NLME models, particularly when convergence is a concern in model selection.

关键词： Cunninghamia lanceolata expectation-maximization algorithm first-order conditional expansion Lindstrom and Bates algorithm nonlinear mixed-effects models orange tree data simulated data

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Sparse cluster analysis of large-scale discrete variables with application to single nucleotide polymorphism data

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JOURNAL OF APPLIED STATISTICS 2013年第2期40卷 358-367页

作者： Wu, Baolin Univ Minnesota Sch Publ Hlth Div Biostat Minneapolis MN 55455 USA

Currently, extreme large-scale genetic data present significant challenges for cluster analysis. Most of the existing clustering methods are typically built on the Euclidean distance and geared toward analyzing continuous response. They work well for clustering, e.g. microarray gene expression data, but often perform poorly for clustering, e.g. large-scale single nucleotide polymorphism (SNP) data. In this paper, we study the penalized latent class model for clustering extremely large-scale discrete data. The penalized latent class model takes into account the discrete nature of the response using appropriate generalized linear models and adopts the lasso penalized likelihood approach for simultaneous model estimation and selection of important covariates. We develop very efficient numerical algorithms for model estimation based on the iterative coordinate descent approach and further develop the expectationmaximization algorithm to incorporate and model missing values. We use simulation studies and applications to the international HapMap SNP data to illustrate the competitive performance of the penalized latent class model.

关键词： clustering expectation-maximization algorithm latent class model lasso k-means principal components single nucleotide polymorphism sparse clustering

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Case-sibling studies that acknowledge unstudied parents and permit the inclusion of unmatched individuals

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INTERNATIONAL JOURNAL OF EPIDEMIOLOGY 2013年第1期42卷 298-307页

作者： Shi, Min Umbach, David M. Weinberg, Clarice R. NIEHS Biostat Branch NIH Res Triangle Pk NC USA

Background Family-based designs enable assessment of genetic associations without bias from population stratification. When parents are not readily available - especially for diseases with onset later in life - the case-sibling design, where each case is matched with one or more unaffected siblings, is useful. Analysis typically accounts for within-family dependencies by using conditional logistic regression (CLR). Methods We consider an alternative to CLR that treats each case-sibling set as a nuclear family with both parents missing by design. One can carry out maximum likelihood analysis by using the expectation-maximization (EM) algorithm to account for missing parental genotypes. We compare conditional logistic regression and the missing-parents approach under several risk scenarios. Results We show that the missing-parents approach improves power when some families have more than one unaffected sibling and that under weak assumptions the approach permits the incorporation of supplemental cases who have no sibling available and supplemental controls whose case sibling is unavailable (e.g., due to disability or death). Conclusion The missing-parents approach offers both improved statistical efficiency and asymptotically unbiased estimation for genotype relative risks and genotype-by-exposure interaction parameters.

关键词： Genetic association case-sibling missing parents expectation-maximization algorithm conditional logistic regression

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Estimating time-varying effects for overdispersed recurrent events data with treatment switching

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BIOMETRIKA 2013年第2期100卷 339-354页

作者： Chen, Qingxia Zeng, Donglin Ibrahim, Joseph G. Akacha, Mouna Schmidli, Heinz Vanderbilt Univ Dept Biostat Nashville TN 37232 USA Univ N Carolina Dept Biostat Chapel Hill NC 27516 USA Novartis Pharma AG Stat Methodol CH-4002 Basel Switzerland

In the analysis of multivariate event times, frailty models assuming time-independent regression coefficients are often considered, mainly due to their mathematical convenience. In practice, regression coefficients are often time dependent and the temporal effects are of clinical interest. Motivated by a phase III clinical trial in multiple sclerosis, we develop a semiparametric frailty modelling approach to estimate time-varying effects for overdispersed recurrent events data with treatment switching. The proposed model incorporates the treatment switching time in the time-varying coefficients. Theoretical properties of the proposed model are established and an efficient expectation-maximization algorithm is derived to obtain the maximum likelihood estimates. Simulation studies evaluate the numerical performance of the proposed model under various temporal treatment effect curves. The ideas in this paper can also be used for time-varying coefficient frailty models without treatment switching as well as for alternative models when the proportional hazard assumption is violated. A multiple sclerosis dataset is analysed to illustrate our methodology.

关键词： B-spline expectation-maximization algorithm Maximum likelihood estimate Recurrent event Time-varying coefficient Treatment switching

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MULTIPLICATIVE UPDATES FOR MODELING MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN

MULTIPLICATIVE UPDATES FOR MODELING MIXTURES OF NON-STATIONA...

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21st European Signal Processing Conference (EUSIPCO)

作者： Badeau, Roland Ozerov, Alexey Telecom ParisTech Inst Mines Telecom CNRS LTCI Paris France Technicolor Res & Innovat Paris France

ISBN: (纸本)9781479936878

We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for representing mixtures of non-stationary signals in the time-frequency domain, and we highlighted its capability to both reach a high spectral resolution and reconstruct high quality audio signals. An expectation-maximization (EM) algorithm was also proposed for estimating its parameters. In this paper, we replace the maximization step by multiplicative update rules (MUR), in order to improve the convergence rate. We also introduce general MUR that are not limited to nonnegative parameters, and we propose a new insight into the EM algorithm, which shows that MUR and EM actually belong to the same family. We thus introduce a continuum of algorithms between them. Experiments confirm that the proposed approach permits to overcome the convergence rate of the EM algorithm.

关键词： Nonnegative Matrix Factorization High Resolution methods expectation-maximization algorithm Multiplicative update rules

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Noise Benefits in Backpropagation and Deep Bidirectional Pre-training

Noise Benefits in Backpropagation and Deep Bidirectional Pre...

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International Joint Conference on Neural Networks (IJCNN)

作者： Audhkhasi, Kartik Osoba, Osonde Kosko, Bart Univ So Calif Signal & Image Proc Inst Dept Elect Engn Los Angeles CA 90089 USA

ISBN: (纸本)9781467361293;9781467361286

We prove that noise can speed convergence in the backpropagation algorithm. The proof consists of two separate results. The first result proves that the backpropagation algorithm is a special case of the generalized expectationmaximization (EM) algorithm for iterative maximum likelihood estimation. The second result uses the recent EM noise benefit to derive a sufficient condition for backpropagation training. The noise adds directly to the training data. A noise benefit also applies to the deep bidirectional pre-training of the neural network as well as to the backpropagation training of the network. The geometry of the noise benefit depends on the probability structure of the neurons at each layer. Logistic sigmoidal neurons produce a forbidden noise region that lies below a hyperplane. Then all noise on or above the hyperplane can only speed convergence of the neural network. The forbidden noise region is a sphere if the neurons have a Gaussian signal or activation function. These noise benefits all follow from the general noise benefit of the EM algorithm. Monte Carlo sample means estimate the population expectations in the EM algorithm. We demonstrate the noise benefits using MNIST digit classification.

关键词： Backpropagation bidirectional associative memory noise benefit neural network stochastic resonance expectation-maximization algorithm

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VARIATIONAL BAYESIAN EM algorithm FOR MODELING MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN (HR-NMF)

VARIATIONAL BAYESIAN EM ALGORITHM FOR MODELING MIXTURES OF N...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Badeau, Roland Dremeau, Angelique CNRS LTCI Telecom ParisTech Inst Mines Telecom Paris France

ISBN: (纸本)9781479903566

We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for analyzing mixtures of non-stationary signals in the time-frequency domain, and highlighted its capability to both reach high spectral resolution and reconstruct high quality audio signals. In order to estimate the model parameters and the latent components, we proposed to resort to an expectation-maximization (EM) algorithm based on a Kalman filter/smoother. The approach proved to be appropriate for modeling audio signals in applications such as source separation and audio inpainting. However, its computational cost is high, dominated by the Kalman filter/smoother, and may be prohibitive when dealing with high-dimensional signals. In this paper, we consider two different alternatives, using the variational Bayesian EM algorithm and two mean-field approximations. We show that, while significantly reducing the complexity of the estimation, these novel approaches do not alter its quality.

关键词： Nonnegative Matrix Factorization High Resolution methods expectation-maximization algorithm Variational inference

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