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

共 448 条记录，以下是401-410 订阅

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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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Robust clustering of data collected via crowdsourcing

Robust clustering of data collected via crowdsourcing

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Alba Pages-Zamora Georgios B. Giannakis Roberto Lopez-Valcarce Pere Gimenez-Febrer SPCOM Group Universitat Politecnica de Catalunya-Barcelona Tech Spain Dept. of ECE and DTC University of Minnesota Minneapolis USA GPSC Universidade de Vigo Spain

ISBN: (纸本)9781509041183

Crowdsourcing approaches rely on the collection of multiple individuals to solve problems that require analysis of large data sets in a timely accurate manner. The inexperience of participants or annotators motivates well robust techniques. Focusing on clustering setups, the data provided by all annotators is suitably modeled here as a mixture of Gaussian components plus a uniformly distributed random variable to capture outliers. The proposed algorithm is based on the expectation-maximization algorithm and allows for soft assignments of data to clusters, to rate annotators according to their performance, and to estimate the number of Gaussian components in the non-Gaussian/Gaussian mixture model, in a jointly manner.

关键词： Crowdsourcing Gaussian plus non-Gaussian Mixture Outlier EM algorithm Bayesian Information Criterion expectation-maximisation algorithm outliers Crowdsource Bayesian information criteria Random variable Cluster Analysis Gauss

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Latent Semantic Transliteration using Dirichlet Mixture 12

Latent Semantic Transliteration using Dirichlet Mixture

引用

Named Entity Workshop

作者： Masato Hagiwara Satoshi Sekine Rakuten Institute of Technology New York 215 Park Avenue South New York NY

ISBN: (纸本)9781627483476

Transliteration has been usually recognized by spelling-based supervised models. However, a single model cannot deal with mixture of words with different origins, such as "get" in "piaget" and "target". Li et al. (2007) propose a class transliteration method, which explicitly models the source language origins and switches them to address this issue. In contrast to their model which requires an explicitly tagged training corpus with language origins, Hagiwara and Sekine (2011) have proposed the latent class transliteration model, which models language origins as latent classes and train the transliteration table via the EM algorithm. However, this model, which can be formulated as uni-gram mixture, is prone to overfitting since it is based on maximum likelihood estimation. We propose a novel latent semantic transliteration model based on Dirichlet mixture, where a Dirichlet mixture prior is introduced to mitigate the overfitting problem. We have shown that the proposed method considerably outperform the conventional transliteration models.

关键词： origins Semantics expectation-maximisation algorithm maximum likelihood estimation Switches original language Lithium

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Parameter Estimation over Wired Networks with Time-Delay and Packet Collision

Parameter Estimation over Wired Networks with Time-Delay and...

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CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

作者： Felipe Arancibia Rodrigo Carvajal Juan C. Agüero Departamento de Electrónica Universidad Técnica Federico Santa María Chile

ISBN: (纸本)9781728131863

In this paper, we address a parameters estimation problem of a dynamic system using an Output Error model, in which the output signal is transmitted through wired networks. We consider that the wired network may cause unknown delays and collision packet in data. We use the Akaike information criterion to estimate the time delay caused by wired network. Furthermore, due to the packet loss caused by wired network, we use expectation Maximization algorithm to estimate the dynamic system parameters. Finally, we test the proposed algorithm effectiveness with numerical examples and a sensitivity analysis.

关键词： delays expectation-maximisation algorithm parameter estimation sensitivity analysis expectation-maximisation algorithm Parameter estimation wired network Sensitivity analysis time delay Dynamic systems packet collision outgoing signal delay error model

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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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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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A Probabilistic Framework for Deep Learning 16

A Probabilistic Framework for Deep Learning

引用

Annual Conference on Neural Information Processing Systems

作者： Ankit B. Patel Tan Nguyen Richard G. Baraniuk Baylor College of Medicine Rice University Rice University

ISBN: (纸本)9781510838819

We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in data due to latent task nuisance variables. We demonstrate that max-sum inference in the DRMM yields an algorithm that exactly reproduces the operations in deep convolutional neural networks (DCNs), providing a first principles derivation. Our framework provides new insights into the successes and shortcomings of DCNs as well as a principled route to their improvement. DRMM training via the expectation-Maximization (EM) algorithm is a powerful alternative to DCN back-propagation, and initial training results are promising. Classification based on the DRMM and other variants outperforms DCNs in supervised digit classification, training 2-3 x faster while achieving similar accuracy. Moreover, the DRMM is applicable to semi-supervised and unsupervised learning tasks, achieving results that are state-of-the-art in several categories on the MNIST benchmark and comparable to state of the art on the CIFAR10 benchmark.

关键词： Frameworks Probability state of the art BENCHMARKS Machine Learning initial training Arts Unsupervised learning expectation-maximisation algorithm

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An innovative multimodal/multispectral image registration method for medical images based on the expectation-maximization algorithm

An innovative multimodal/multispectral image registration me...

引用

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

作者： Edgar Arce-Santana Daniel U. Campos-Delgado Aldo Mejia-Rodriguez Isnardo Reducindo Facultad de Ciencias Universidad Autonoma de San Luis Potosi SLP Mexico

ISBN: (纸本)9781424492695

In this paper, we present a methodology for multimodal/multispectral image registration of medical images. This approach is formulated by using the expectation-Maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

关键词： EM medical images based expectation-maximization algorithm expectation-maximisation algorithm image registration Geometric transformation images Medical imaging Medical intensity distribution

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Bin-EM-CEM algorithms of spherical parsimonious Gaussian mixture models for binned data clustering

Bin-EM-CEM algorithms of spherical parsimonious Gaussian mix...

引用

IEEE International Conference on Intelligent Engineering Systems

作者： Hani Hamdan Jingwen Wu SUPELEC Department of Signal Processing and Electronic Systems

ISBN: (纸本)9781479908271

EM algorithm is widely used in clustering domain because of its easy implementation and small storage space. CEM algorithm, which is considered as a classification version of EM algorithm, is another common used clustering algorithm. With the development of technology, we obtain more and more data. This results in slow computation of EM and CEM algorithms. Binning data seems to be efficient in gaining computation time by reducing the number of observations to the number of bins. Thus, EM and CEM algorithms applied to binned data were proposed: binned-EM and bin-EM-CEM algorithms. Moreover, fourteen parsimonious Gaussian mixture models, generated according to eigenvalue decomposition of the variance matrices of the mixture components, have less parameters than the most general model. By applying the EM and CEM algorithms of parsimonious models, estimation process is simplified and then accelerated. In this paper, to combine the advantages of binned data and parsimonious Gaussian mixture models, we develop bin-EM-CEM algorithms of spherical parsimonious Gaussian mixture models.

关键词： expectation-maximisation algorithm algorithms data clustering Sphericity Clustering algorithms

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A vessel segmentation method for MRA data based on probabilistic mixture model

A vessel segmentation method for MRA data based on probabili...

引用

2015 IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015)

作者： Pei Lu Cheng Wang Shoujun Zhou Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Accurate segmentation and visualization of cerebral vessels have important significance to the diagnosis and treatment of relevant brain diseases. However, some segmentation algorithms are only competent for the standard data. In this paper, a segmentation method based on probabilistic mixture model is proposed to solve clinical problems. Through histogram analysis of the magnetic resonance angiography (MRA) data offered by Guangzhou General Hospital of the Chinese PLA, a mixture model formed by six probabilistic distributions (one Exponential, one Rayleigh, and four Normal distributions) was built to fit the histogram curve. Least squares and expectation maximization (EM) algorithms have been used for parameters estimation. At last, the segmentation was enhanced by maximum a posteriori probability (MAP) and Markov random field (MRF) algorithm. The effectiveness of the proposed method has been validated by segmentation tests on a series of clinical MRA data with good performance.

关键词： EM algorithm MRF Vessel segmentation histogram analysis mixture model Mixture models expectation-maximisation algorithm Probability histograms MYRF gene Maximum a posteriori estimation Parameter estimation cerebrovascular system

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