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检索条件"主题词=Expectation-Maximisation algorithm"

共 493 条记录，以下是251-260 订阅

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AN INTEGRATION OF SOURCE LOCATION CUES FOR SPEECH CLUSTERING IN DISTRIBUTED MICROPHONE ARRAYS

AN INTEGRATION OF SOURCE LOCATION CUES FOR SPEECH CLUSTERING...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mehrez Souden Keisuke Kinoshita Tomohiro Nakatani NTT Communication Science Laboratories Kyoto Japan

ISBN: (纸本)9781479903573

We propose a new approach for clustering competing speech sources using distributed microphone arrays. In this approach, we first define two feature vectors where the first captures the intra-node location information while the second captures the level difference of speech energy recorded at different nodes. Then, we introduce Watson and Dirichlet mixture models to model the first and second features, respectively. We integrate both types of information in an expectation maximization algorithm to cluster the simultaneous speech sources. The performance of the proposed approach is superior to best node selection and comparable to centralized processing in terms of conventional blind source separation metrics.

关键词： Speech expectation-maximisation algorithm Position measurement Microphone Device Component centralized processing microphone arrays POSITION INFORMATION Eigenvector information-type integration Cluster Analysis

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Vectorcardiographic loop alignment for fetal movement detection using the expectation-Maximization algorithm and Support Vector Machines

Vectorcardiographic loop alignment for fetal movement detect...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： R. Vullings M. Mischi Faculty of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (纸本)9781457702150

Reduced fetal movement is an important parameter to assess fetal distress. Currently, no suitable methods are available that can objectively assess fetal movement during pregnancy. Fetal vectorcardiographic (VCG) loop alignment could be such a method. In general, the goal of VCG loop alignment is to correct for motion-induced changes in the VCGs of (multiple) consecutive heartbeats. However, the parameters used for loop alignment also provide information to assess fetal movement. Unfortunately, current methods for VCG loop alignment are not robust against low-quality VCG signals. In this paper, a more robust method for VCG loop alignment is developed that includes a priori information on the loop alignment, yielding a maximum a posteriori loop alignment. Classification, based on movement parameters extracted from the alignment, is subsequently performed using support vector machines, resulting in correct classification of (absence of) fetal movement in about 75% of cases. After additional validation and optimization, this method can possibly be employed for continuous fetal movement monitoring.

关键词： Fetal Movement Alignment Support Vector Network expectation-maximisation algorithm Moving parameter

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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...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Roland Badeau Angelique Dremeau Institut Mines-Telecom Telecom ParisTech CNRS LTCI

ISBN: (纸本)9781479903573

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 expectation-maximisation algorithm Variational Inference matrix decomposition audio signals signals mixtures Signal timing Modeling Source separation Spectral resolution model parameters Audio nonstationary random signal

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A GENERATIVE SEMI-SUPERVISED MODEL FOR MULTI-VIEW LEARNING WHEN SOME VIEWS ARE LABEL-FREE

A GENERATIVE SEMI-SUPERVISED MODEL FOR MULTI-VIEW LEARNING W...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Gaole Jin Raviv Raich David J. Miller School of EECS Oregon State University EE Dept. The Pennsylvania State University

ISBN: (纸本)9781479903573

We consider multi-view classification for the challenging scenario where, for some views, there are no labeled training examples. Several discriminative approaches have been recently proposed for special instances of this problem. Here, alternatively, we propose a generative semi-supervised mixture model across all views which, via marginalization, flexibly performs exact class inference, given any subset of available views. The proposed model is an extension of semi-supervised mixtures to a multi-view setting, as well as a semi-supervised extension of mixtures of factors analyzers (MFA)[1]. A novel EM algorithm with a computationally efficient E-step is derived for learning our multi-view model. Specialization of this formulation to the standard MFA problem also gives a reduced complexity E-step, compared to the original EM algorithm proposed for MFA. Our multi-view method is experimentally demonstrated on digit recognition using audio and lip video views, achieving competitive results with alternative, discriminative approaches.

关键词： Multi-view learning Semi-supervised learning Mixture of factors analyzers expectation-Maximization Semi-supervised Learning expectation-maximisation algorithm Learning multifibre agreement View Audio mixtures

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A PROBABILISTIC FRAMEWORK FOR MULTIPLE SPEAKER LOCALIZATION

A PROBABILISTIC FRAMEWORK FOR MULTIPLE SPEAKER LOCALIZATION

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Youssef Oualil Mathew Magimai.-Doss Friedrich Faubel Dietrich Klakow Spoken Language Systems Saarland University Idiap Research Institute

ISBN: (纸本)9781479903573

This paper presents a novel probabilistic framework for localizing multiple speakers with a microphone array. In this framework, the generalized cross correlation function (GCC) of each microphone pair is interpreted as a probability distribution of the time difference of arrival (TDOA) and subsequently approximated as a Gaussian mixture. The distribution parameters are estimated with a weighted expectation maximization algorithm. Then, the joint distribution of the TDOA Gaussian mixtures is mapped to a multimodal distribution in the location space, where each mode represents a potential source location. The approach taken here performs the localization by 1) reducing the search space to some regions that are likely to contain a source and then 2) extracting the actual speaker locations with a numerical optimization algorithm. The effectiveness of the proposed approach is shown using the AV16.3 corpus.

关键词： Microphone arrays Localization Multiple speakers Gaussian mixture Steered response power microphone arrays Loudspeakers Time difference of arrival expectation-maximisation algorithm Probability distribution distributed parameter Position measurement TimeDif Optimization algorithms

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REM-Seg: A Robust EM algorithm for Parallel Segmentation and Registration of Point Clouds

REM-Seg: A Robust EM Algorithm for Parallel Segmentation and...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Benjamin Eckart Alonzo Kelly Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781467363563

For purposes of real-time 3D sensing, it is important to be able to quickly register together incoming point cloud data. In this paper, we devise a method to quickly and robustly decompose large point clouds into a relatively small number of meaningful surface patches from which we register new data points. The surface patch representation sidesteps the costly problem of matching points to points since incoming data only need to be compared with the patches. The chosen parametrization of the patches (as Gaussians) leads to a smooth data likelihood function with a well-defined gradient. This representation thus forms the basis for a robust and efficient registration algorithm using a parallelized gradient descent implemented on a GPU using CUDA. We use a modified Gaussian Mixture Model (GMM) formulation solved by expectation Maximization (EM) to segment the point cloud and an annealing gradient descent method to find the 6-DOF rigid transformation between the incoming point cloud and the segmented set of surface patches. We test our algorithm, Robust EM Segmentation (REM-Seg), against other GPU-accelerated registration algorithms on simulated and real data and show that our method scales well to large numbers of points, has a wide range of convergence, and is suitably accurate for 3D registration.

关键词： Parallel Segmentation Registration EM Registration expectation-maximisation algorithm Clouds Large Numbers Patches wide-range Robustness Gauss

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NOISE ROBUST SPEECH DEREVERBERATION WITH KALMAN SMOOTHER

NOISE ROBUST SPEECH DEREVERBERATION WITH KALMAN SMOOTHER

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Masahito Togami Yohei Kawaguchi Central Research Laboratory Hitachi Ltd. 1-280 Higashi-koigakubo Kokubunji-shi Tokyo 185-8601 Japan

ISBN: (纸本)9781479903573

A speech dereverberation method is proposed that is robust against background noise. In contrast to conventional methods based on the linear prediction of the given microphone input signal, in which the linear prediction coefficients are not fully optimized when there is background noise, the proposed method optimizes the coefficients by linear prediction of the noiseless reverberant speech signal even when there is background noise. The noiseless reverberant speech signal and the parameters are iteratively updated on the basis of the expectation maximization algorithm. In the expectation step, sufficient statistics of latent variables which include noiseless reverberant speech signal are estimated using the Kalman smoother. Unlike the standard Kalman smoother, which uses a time-invariant covariance matrix as a state-transition covariance matrix, the proposed method utilizes a time-varying covariance matrix, enabling it to meet the time-varying speech characteristics. The parameters are updated so that the Q function is increased in the maximization step. Experimental results show that the proposed method is superior to conventional methods under noisy conditions.

关键词： Voice Signal linear prediction BACKGROUND NOISE Q FUNCTIONS expectation-maximisation algorithm Noise variance covariance matrix Speech Characteristics Speech Covariance matrix

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Relevance Topic Model for Unstructured Social Group Activity Recognition 13

Relevance Topic Model for Unstructured Social Group Activity...

引用

Annual Conference on Neural Information Processing Systems

作者： Fang Zhao Yongzhen Huang Liang Wang Tieniu Tan Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781632660244

Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data. To tackle this problem, we propose a "relevance topic model" for jointly learning meaningful mid-level representations upon bag-of-words (BoW) video representations and a classifier with sparse weights. In our approach, sparse Bayesian learning is incorporated into an undirected topic model (i.e., Replicated Softmax) to discover topics which are relevant to video classes and suitable for prediction. Rectified linear units are utilized to increase the expressive power of topics so as to explain better video data containing complex contents and make variational inference tractable for the proposed model. An efficient variational EM algorithm is presented for model parameter estimation and inference. Experimental results on the Unstructured Social Activity Attribute dataset show that our model achieves state of the art performance and outperforms other supervised topic model in terms of classification accuracy, particularly in the case of a very small number of labeled training videos.

关键词： social group Web video Bayesian learning video data expectation-maximisation algorithm Classifiers linear unit Training Videos state of the art Themes Variational Inference

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Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization algorithms 13

Probabilistic Low-Rank Matrix Completion with Adaptive Spect...

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Annual Conference on Neural Information Processing Systems

作者： Adrien Todeschini Francois Caron Marie Chavent INRIA - IMB - Univ. Bordeaux 33405 Talence France Univ. Oxford Dept. of Statistics Oxford OX1 3TG UK Univ. Bordeaux - IMB - INRIA 33000 Bordeaux France

ISBN: (纸本)9781632660244

We propose a novel class of algorithms for low rank matrix completion. Our approach builds on novel penalty functions on the singular values of the low rank matrix. By exploiting a mixture model representation of this penalty, we show that a suitably chosen set of latent variables enables to derive an expectation-Maximization algorithm to obtain a Maximum A Posteriori estimate of the completed low rank matrix. The resulting algorithm is an iterative soft-thresholded algorithm which iteratively adapts the shrinkage coefficients associated to the singular values. The algorithm is simple to implement and can scale to large matrices. We provide numerical comparisons between our approach and recent alternatives showing the interest of the proposed approach for low rank matrix completion.

关键词： rank of matrix algorithms matrices Rank of data Completion contraction coefficient expectation-maximisation algorithm penalty function penalty Singular Probability matrix

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A DIFFUSION-BASED DISTRIBUTED EM algorithm FOR DENSITY ESTIMATION IN WIRELESS SENSOR NETWORKS

A DIFFUSION-BASED DISTRIBUTED EM ALGORITHM FOR DENSITY ESTIM...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Silvana Silva Pereira Alba Pages-Zamora Roberto Lopez-Valcarce SPCOM Group Universitat Politecnica de Catalunya-Barcelona Tech GPSC Universidade de Vigo

ISBN: (纸本)9781479903573

Distributed implementations of the expectation-Maximization (EM) algorithm reported in literature have been proposed for applications to solve specific problems. In general, a primary requirement to derive a distributed solution is that the structure of the centralized version enables the computation involving global information in a distributed fashion. This paper treats the problem of distributed estimation of Gaussian densities by means of the EM algorithm in wireless sensor networks using diffusion strategies, where the information is gradually diffused across the network for the computation of the global functions. The low-complexity implementation presented here is based on a two time scale operation for information averaging and diffusion. The convergence to a fixed point of the centralized solution has been studied and the appealing results motivate our choice for this model. Numerical examples provided show that the performance of the distributed EM is, in practice, equal to that of the centralized scheme.

关键词： Distributed algorithms expectation-maximization algorithms Gaussian distribution Unsupervised learning Wireless sensor networks expectation-maximisation algorithm Wireless sensor networks Unsupervised learning distributed algorithms Gaussian distribution Distribute Radio

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