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Dissertations / Theses on the topic 'Finite Gaussian mixture model'

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Published: 3 June 2025
Last updated: 20 July 2025

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1

Malsiner-Walli, Gertraud, Sylvia Frühwirth-Schnatter, and Bettina Grün. "Model-based clustering based on sparse finite Gaussian mixtures." Springer, 2016. http://dx.doi.org/10.1007/s11222-014-9500-2.

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In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number
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Stewart, Michael Ian. "Asymptotic methods for tests of homogeneity for finite mixture models." Thesis, The University of Sydney, 2002. http://hdl.handle.net/2123/855.

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We present limit theory for tests of homogeneity for finite mixture models. More specifically, we derive the asymptotic distribution of certain random quantities used for testing that a mixture of two distributions is in fact just a single distribution. Our methods apply to cases where the mixture component distributions come from one of a wide class of one-parameter exponential families, both continous and discrete. We consider two random quantities, one related to testing simple hypotheses, the other composite hypotheses. For simple hypotheses we consider the maximum of the standard
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Stewart, Michael Ian. "Asymptotic methods for tests of homogeneity for finite mixture models." University of Sydney. Mathematics and Statistics, 2002. http://hdl.handle.net/2123/855.

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We present limit theory for tests of homogeneity for finite mixture models. More specifically, we derive the asymptotic distribution of certain random quantities used for testing that a mixture of two distributions is in fact just a single distribution. Our methods apply to cases where the mixture component distributions come from one of a wide class of one-parameter exponential families, both continous and discrete. We consider two random quantities, one related to testing simple hypotheses, the other composite hypotheses. For simple hypotheses we consider the maximum of the standard
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Stewart, Michael. "Asymptotic methods for tests of homogeneity for finite mixture models." Connect to full text, 2002. http://hdl.handle.net/2123/855.

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Thesis (Ph. D.)--University of Sydney, 2002.<br>Title from title screen (viewed Apr. 28, 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Mathematics and Statistics, Faculty of Science. Includes bibliography. Also available in print form.
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Safont, Armero Gonzalo. "New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods." Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/53913.

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[EN] This thesis considers new applications of non-Gaussian mixtures in the framework of statistical signal processing and pattern recognition. The non-Gaussian mixtures were implemented by mixtures of independent component analyzers (ICA). The fundamental hypothesis of ICA is that the observed signals can be expressed as a linear transformation of a set of hidden variables, usually referred to as sources, which are statistically independent. This independence allows factoring the original M-dimensional probability density function (PDF) of the data as a product of one-dimensional probability
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Gabriel, Naveen. "Automatic Speech Recognition in Somali." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166216.

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The field of speech recognition during the last decade has left the research stage and found its way into the public market, and today, speech recognition software is ubiquitous around us. An automatic speech recognizer understands human speech and represents it as text. Most of the current speech recognition software employs variants of deep neural networks. Before the deep learning era, the hybrid of hidden Markov model and Gaussian mixture model (HMM-GMM) was a popular statistical model to solve speech recognition. In this thesis, automatic speech recognition using HMM-GMM was trained on So
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Lan, Jing. "Gaussian mixture model based system identification and control." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0014640.

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Georgescu, Vera. "Classification de données multivariées multitypes basée sur des modèles de mélange : application à l'étude d'assemblages d'espèces en écologie." Phd thesis, Université d'Avignon, 2010. http://tel.archives-ouvertes.fr/tel-00624382.

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En écologie des populations, les distributions spatiales d'espèces sont étudiées afin d'inférer l'existence de processus sous-jacents, tels que les interactions intra- et interspécifiques et les réponses des espèces à l'hétérogénéité de l'environnement. Nous proposons d'analyser les données spatiales multi-spécifiques sous l'angle des assemblages d'espèces, que nous considérons en termes d'abondances absolues et non de diversité des espèces. Les assemblages d'espèces sont une des signatures des interactions spatiales locales des espèces entre elles et avec leur environnement. L'étude des assem
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9

Vakil, Sam. "Gaussian mixture model based coding of speech and audio." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=81575.

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The transmission of speech and audio over communication channels has always required speech and audio coders with reasonable search and computational complexity and good performance relative to the corresponding distortion measure.<br>This work introduces a coding scheme which works in a perceptual auditory domain. The input high dimensional frames of audio and speech are transformed to power spectral domain, using either DFT or MDCT. The log spectral vectors are then transformed to the excitation domain. In the quantizer section the vectors are DCT transformed and decorrelated. This op
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Sadarangani, Nikhil 1979. "An improved Gaussian mixture model algorithm for background subtraction." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87293.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (leaves 71-72).<br>by Nikhil Sadarangani.<br>M.Eng.
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Stuttle, Matthew Nicholas. "A gaussian mixture model spectral representation for speech recognition." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620077.

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12

Wang, Juan. "Estimation of individual treatment effect via Gaussian mixture model." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/839.

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In this thesis, we investigate the estimation problem of treatment effect from Bayesian perspective through which one can first obtain the posterior distribution of unobserved potential outcome from observed data, and then obtain the posterior distribution of treatment effect. We mainly consider how to represent a joint distribution of two potential outcomes - one from treated group and another from control group, which can give us an indirect impression of correlation, since the estimation of treatment effect depends on correlation between two potential outcomes. The first part of this thesis
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Lu, Liang. "Subspace Gaussian mixture models for automatic speech recognition." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8065.

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In most of state-of-the-art speech recognition systems, Gaussian mixture models (GMMs) are used to model the density of the emitting states in the hidden Markov models (HMMs). In a conventional system, the model parameters of each GMM are estimated directly and independently given the alignment. This results a large number of model parameters to be estimated, and consequently, a large amount of training data is required to fit the model. In addition, different sources of acoustic variability that impact the accuracy of a recogniser such as pronunciation variation, accent, speaker factor and en
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Delport, Marion. "A spatial variant of the Gaussian mixture of regressions model." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/65883.

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In this study the nite mixture of multivariate Gaussian distributions is discussed in detail including the derivation of maximum likelihood estimators, a discussion on identi ability of mixture components as well as a discussion on the singularities typically occurring during the estimation process. Examples demonstrate the application of the nite mixture of univariate and bivariate Gaussian distributions. The nite mixture of multivariate Gaussian regressions is discussed including the derivation of maximum likelihood estimators. An example is used to demonstrate the application of the mixt
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Marek, Petr. "Gaussian mixtures in R." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-193077.

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Using Gaussian mixtures is a popular and very flexible approach to statistical modelling. The standard approach of maximum likelihood estimation cannot be used for some of these models. The estimates are, however, obtainable by iterative solutions, such as the EM (Expectation-Maximization) algorithm. The aim of this thesis is to present Gaussian mixture models and their implementation in R. The non-trivial case of having to use the EM algorithm is assumed. Existing methods and packages are presented, investigated and compared. Some of them are extended by custom R code. Several exhaustive simu
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Zhang, Shiju. "Statistical Inferences under a semiparametric finite mixture model." See Full Text at OhioLINK ETD Center (Requires Adobe Acroba Reader for viewing), 2005. http://www.ohiolink.edu/etd/view.cgi?toledo1135779503.

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Thesis (Ph.D.)--University of Toledo, 2005.<br>Typescript. "A dissertation [submitted] as partial fulfillment of the requirements of the Doctor of Philosophy degree in Mathematics." Bibliography: leaves 100-105.
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Soegiarso, Restuti Widayati. "A covariate model in finite mixture survival distributions." Case Western Reserve University School of Graduate Studies / OhioLINK, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=case1056547652.

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18

Manikas, Vasileios. "A Bayesian Finite Mixture Model for Network-Telecommunication Data." Thesis, Stockholms universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-146039.

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A data modeling procedure called Mixture model, is introduced beneficial to the characteristics of our data. Mixture models have been proved flexible and easy to use, a situation which can be confirmed from the majority of papers and books which have been published the last twenty years. The models are estimated using a Bayesian inference through an efficient Markov Chain Monte Carlo (MCMC) algorithm, known as Gibbs Sampling. The focus of the paper is on models for network-telecommunication lab data (not time dependent data) and on the valid predictions we can accomplish. We categorize our var
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Meynet, Caroline. "Sélection de variables pour la classification non supervisée en grande dimension." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00752613.

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Il existe des situations de modélisation statistique pour lesquelles le problème classique de classification non supervisée (c'est-à-dire sans information a priori sur la nature ou le nombre de classes à constituer) se double d'un problème d'identification des variables réellement pertinentes pour déterminer la classification. Cette problématique est d'autant plus essentielle que les données dites de grande dimension, comportant bien plus de variables que d'observations, se multiplient ces dernières années : données d'expression de gènes, classification de courbes... Nous proposons une procédu
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Tran, Denis. "A study of bit allocation for Gaussian mixture model quantizers and image coders /." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=83937.

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This thesis describes different bit allocation schemes and their performances when applied on coding line spectral frequencies (LSF) using the GMM-based coder designed by Subramaniam and a simple image transform coder. The new algorithms are compared to the original bit allocation formula; the Pruning algorithm used by Subramaniam, Segall's method and the Greedy bit allocation algorithm using the Log Spectral Distortion and the Mean-Square Error for the LSF quantizer and the Peak Signal-to-Noise Ratio for the image coder.<br>First, a Greedy level allocation algorithm is developed based o
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Shashidhar, Sanda, and Amirisetti Sravya. "Online Handwritten Signature Verification System : using Gaussian Mixture Model and Longest Common Sub-Sequences." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15807.

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Moradiannejad, Ghazaleh. "People Tracking Under Occlusion Using Gaussian Mixture Model and Fast Level Set Energy Minimization." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/24304.

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Tracking multiple articulated objects (such as a human body) and handling occlusion between them is a challenging problem in automated video analysis. This work proposes a new approach for accurately and steadily visual tracking people, which should function even if the system encounters occlusion in video sequences. In this approach, targets are represented with a Gaussian mixture, which are adapted to regions of the target automatically using an EM-model algorithm. Field speeds are defined for changed pixels in each frame based on the probability of their belonging to a particular person's b
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Webb, Grayson. "A Gaussian Mixture Model based Level Set Method for Volume Segmentation in Medical Images." Thesis, Linköpings universitet, Beräkningsmatematik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148548.

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This thesis proposes a probabilistic level set method to be used in segmentation of tumors with heterogeneous intensities. It models the intensities of the tumor and surrounding tissue using Gaussian mixture models. Through a contour based initialization procedure samples are gathered to be used in expectation maximization of the mixture model parameters. The proposed method is compared against a threshold-based segmentation method using MRI images retrieved from The Cancer Imaging Archive. The cases are manually segmented and an automated testing procedure is used to find optimal parameters f
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Lindström, Kevin. "Fault Clustering With Unsupervised Learning Using a Modified Gaussian Mixture Model and Expectation Maximization." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176535.

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When a fault is detected in the engine, the check engine light will come on. After that, it is often up to the mechanic to diagnose the engine fault. Manual fault classification by a mechanic can be time-consuming and expensive. Recent technological advancements have granted us immense computing power, which can be utilized to diagnose faults using data-driven classifiers. Data-driven classifiers generally require a lot of training data to be able to accurately diagnose system faults by comparing sensor data to training data because labeled training data is required for a wide variety of diffe
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Dahlqwist, Elisabeth. "Birthweight-specific neonatal health : With application on data from a tertiaryhospital in Tanzania." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-227531.

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The following study analyzes birthweight-specific neonatal health using a combination of a mixture model and logistic regression: the extended Parametric Mixture of Logistic Regression. The data are collected from the Obstetric database at Muhimbili National Hospital in Dar es Salaam, Tanzania and the years 2009 -2013 are used in the analysis. Due to rounding in the birthweight data a novel method to adjust for rounding when estimating a mixture model is applied. The influence of rounding on the estimates is then investigated. A three-component model is selected. The variables used in the anal
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Kawaguchi, Nobuo, Katsuhiko Kaji, Susumu Fujita, 信夫 河口, 克彦 梶 та 迪. 藤田. "Gaussian Mixture Model を用いた無線LAN位置推定手法". 一般社団法人情報処理学会, 2010. http://hdl.handle.net/2237/15430.

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Kawaguchi, Nobuo, Katsuhiko Kaji, Susumu Fujita, 信夫 河口, 克彦 梶 та 迪. 藤田. "Gaussian Mixture Model を用いた無線LAN位置推定手法". 一般社団法人情報処理学会, 2011. http://hdl.handle.net/2237/15440.

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Hatefi, Armin. "Mixture model analysis with rank-based samples." Statistica Sinica, 2013. http://hdl.handle.net/1993/23849.

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Simple random sampling (SRS) is the most commonly used sampling design in data collection. In many applications (e.g., in fisheries and medical research) quantification of the variable of interest is either time-consuming or expensive but ranking a number of sampling units, without actual measurement on them, can be done relatively easy and at low cost. In these situations, one may use rank-based sampling (RBS) designs to obtain more representative samples from the underlying population and improve the efficiency of the statistical inference. In this thesis, we study the theory and application
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Qi, Meng. "Development in Normal Mixture and Mixture of Experts Modeling." UKnowledge, 2016. http://uknowledge.uky.edu/statistics_etds/15.

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In this dissertation, first we consider the problem of testing homogeneity and order in a contaminated normal model, when the data is correlated under some known covariance structure. To address this problem, we developed a moment based homogeneity and order test, and design weights for test statistics to increase power for homogeneity test. We applied our test to microarray about Down’s syndrome. This dissertation also studies a singular Bayesian information criterion (sBIC) for a bivariate hierarchical mixture model with varying weights, and develops a new data dependent information criterio
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Tomashenko, Natalia. "Speaker adaptation of deep neural network acoustic models using Gaussian mixture model framework in automatic speech recognition systems." Thesis, Le Mans, 2017. http://www.theses.fr/2017LEMA1040/document.

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Les différences entre conditions d'apprentissage et conditions de test peuvent considérablement dégrader la qualité des transcriptions produites par un système de reconnaissance automatique de la parole (RAP). L'adaptation est un moyen efficace pour réduire l'inadéquation entre les modèles du système et les données liées à un locuteur ou un canal acoustique particulier. Il existe deux types dominants de modèles acoustiques utilisés en RAP : les modèles de mélanges gaussiens (GMM) et les réseaux de neurones profonds (DNN). L'approche par modèles de Markov cachés (HMM) combinés à des GMM (GMM-HM
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Kullmann, Emelie. "Speech to Text for Swedish using KALDI." Thesis, KTH, Optimeringslära och systemteori, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189890.

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The field of speech recognition has during the last decade left the re- search stage and found its way in to the public market. Most computers and mobile phones sold today support dictation and transcription in a number of chosen languages.  Swedish is often not one of them. In this thesis, which is executed on behalf of the Swedish Radio, an Automatic Speech Recognition model for Swedish is trained and the performance evaluated. The model is built using the open source toolkit Kaldi.  Two approaches of training the acoustic part of the model is investigated. Firstly, using Hidden Markov Model
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Malsiner-Walli, Gertraud, Daniela Pauger, and Helga Wagner. "Effect fusion using model-based clustering." Sage, 2018. http://dx.doi.org/10.1177/1471082X17739058.

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In social and economic studies many of the collected variables are measured on a nominal scale, often with a large number of categories. The definition of categories can be ambiguous and different classification schemes using either a finer or a coarser grid are possible. Categorization has an impact when such a variable is included as covariate in a regression model: a too fine grid will result in imprecise estimates of the corresponding effects, whereas with a too coarse grid important effects will be missed, resulting in biased effect estimates and poor predictive performance. To achieve
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Minh, Tuan Pham, Tomohiro Yoshikawa, Takeshi Furuhashi, and Kaita Tachibana. "Robust feature extractions from geometric data using geometric algebra." IEEE, 2009. http://hdl.handle.net/2237/13896.

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Yang, Chenguang. "Security in Voice Authentication." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/79.

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We evaluate the security of human voice password databases from an information theoretical point of view. More specifically, we provide a theoretical estimation on the amount of entropy in human voice when processed using the conventional GMM-UBM technologies and the MFCCs as the acoustic features. The theoretical estimation gives rise to a methodology for analyzing the security level in a corpus of human voice. That is, given a database containing speech signals, we provide a method for estimating the relative entropy (Kullback-Leibler divergence) of the database thereby establishing the secu
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Zhan, Tingting. "The Generalized Linear Mixed Model for Finite Normal Mixtures with Application to Tendon Fibrilogenesis Data." Diss., Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/171613.

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Statistics<br>Ph.D.<br>We propose the generalized linear mixed model for finite normal mixtures (GLMFM), as well as the estimation procedures for the GLMFM model, which are widely applicable to the hierarchical dataset with small number of individual units and multi-modal distributions at the lowest level of clustering. The modeling task is two-fold: (a). to model the lowest level cluster as a finite mixtures of the normal distribution; and (b). to model the properly transformed mixture proportions, means and standard deviations of the lowest-level cluster as a linear hierarchical structure. W
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Zhao, David Yuheng. "Model Based Speech Enhancement and Coding." Doctoral thesis, Stockholm : Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4412.

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Bekli, Zeid, and William Ouda. "A performance measurement of a Speaker Verification system based on a variance in data collection for Gaussian Mixture Model and Universal Background Model." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20122.

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Voice recognition has become a more focused and researched field in the last century,and new techniques to identify speech has been introduced. A part of voice recognition isspeaker verification which is divided into Front-end and Back-end. The first componentis the front-end or feature extraction where techniques such as Mel-Frequency CepstrumCoefficients (MFCC) is used to extract the speaker specific features of a speech signal,MFCC is mostly used because it is based on the known variations of the humans ear’scritical frequency bandwidth. The second component is the back-end and handles thes
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Bere, Alphonce. "Some non-standard statistical dependence problems." University of the Western Cape, 2016. http://hdl.handle.net/11394/4868.

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Philosophiae Doctor - PhD<br>The major result of this thesis is the development of a framework for the application of pair-mixtures of copulas to model asymmetric dependencies in bivariate data. The main motivation is the inadequacy of mixtures of bivariate Gaussian models which are commonly fitted to data. Mixtures of rotated single parameter Archimedean and Gaussian copulas are fitted to real data sets. The method of maximum likelihood is used for parameter estimation. Goodness-of-fit tests performed on the models giving the highest log-likelihood values show that the models fit the data we
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"Finite Gaussian mixture and finite mixture-of-expert ARMA-GARCH models for stock price prediction." 2003. http://library.cuhk.edu.hk/record=b5891578.

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Tang Him John.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.<br>Includes bibliographical references (leaves 76-80).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgment --- p.iii<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Background --- p.2<br>Chapter 1.1.1 --- Linear Time Series --- p.2<br>Chapter 1.1.2 --- Mixture Models --- p.3<br>Chapter 1.1.3 --- EM algorithm --- p.6<br>Chapter 1.1.4 --- Model Selection --- p.6<br>Chapter 1.2 --- Main Objectives --- p.7<br>Chapter 1.3 --- Outline of this thesis --- p.7<br>Chapter 2 --- Finite Gau
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(8052491), Do hyeung Kim. "MULTI-TARGET TRACKING ALGORITHMS FOR CLUTTERED ENVIRONMENTS." Thesis, 2019.

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<div>Multi-target tracking (MTT) is the problem to simultaneously estimate the number of targets and their states or trajectories. Numerous techniques have been developed for over 50 years, with a multitude of applications in many fields of study; however, there are two most widely used approaches to MTT: i) data association-based traditional algorithms; and ii) finite set statistics (FISST)-based data association free Bayesian multi-target filtering algorithms. Most data association-based traditional filters mainly use a statistical or simple model of the feature without explicitly considerin
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Lai, Chu-Shiuan, and 賴竹煖. "Gaussian Mixture of Background and Shadow Model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/38760050190895130218.

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碩士<br>國立臺灣師範大學<br>資訊工程研究所<br>98<br>In this paper, we integrate shadow information into the background model of a scene in an attempt to detect both shadows and foreground objects at a time. Since shadows accompanying foreground objects are viewed as parts of the foreground objects, shadows will be extracted as well during foreground object detection. Shadows can distort object shapes and may connect multiple objects into one object. On the other hand, shadows tell the directions of light sources. In other words, shadows can be advantageous as well as disadvantageous. To begin, we use an adapti
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Sue, Yung-Chun, and 蘇詠鈞. "Specified Gestures Identification using Gaussian Mixture Model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/15219540833691164661.

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碩士<br>清雲科技大學<br>電子工程所<br>100<br>Sign language recognition technique is composed by the hand images detection and the hand gestures recognition. Hand images detection is locating the sign language select, sign language capture, the palm and fingers part from the sensed image, and rotating them to the appropriate hand posture, both are the important pre-processing for sign language identification and recognition. This paper first introduced sequentially throughout the study practices, as well as the process of image pre-processing instructions. The major work in the hand gestures recognition is
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莊清乾. "Automatic Bird Songs Recognition using Gaussian Mixture Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/09774268339453426682.

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碩士<br>中華大學<br>資訊工程學系(所)<br>96<br>In this paper, Gaussian mixture models (GMM) were applied to identify bird species from their sounds. First, each syllable corresponding to a piece of vocalization is manually segmented. Two-dimension MFCC (TDMFCC), dynamic two-dimension MFCC (DTDMFCC), and normalized audio spectrum envelope (NASE) modulation coefficients are calculated for each syllable and regarded as the vocalization features of each syllable. Principal component analysis (PCA) is used to reduce the feature space dimension of the original input features vector space. GMM is used to cluster t
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LIN, YU-JUNG, and 林昱融. "Modified Gaussian Mixture Model Applied to Speaker Verification." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/33cbau.

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碩士<br>中央警察大學<br>刑事警察研究所<br>104<br>Gaussian mixture model (GMM) is a combination of a plurality of Gaussian probability density function, it can be smoothly approximate the probability density distribution of any arbitrary shape. In various areas of pattern recognition, it has a good recognition results. However, during building the speaker model process, we must determine the parameters of each Gaussian probability density function through constantly iterative calculation, the calculation process is quite complex. This paper presents modified Gaussian mixture model, each characteristic for rec
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Teng, Yu-Hsiang, and 鄧玉祥. "Establishment of Background Image using Gaussian Mixture Model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/28291711624177215723.

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碩士<br>義守大學<br>資訊工程學系碩士班<br>98<br>In the field of object tracking, a background image is often established before further processing. One typical approach for object tracking is to filter out irrelevant background images as well as reserving only the pixel information of the foreground. For such method, it would be more convenient to construct the background image in advance for further processing. Although background subtraction and frame differences are easy to implement, they are easily affected by external environment. For instance, the changes of light source or the sway of leaves could
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Verma, Nishchal Kumar. "Gaussian mixture model based non-additive fuzzy systems." Thesis, 2006. http://localhost:8080/xmlui/handle/12345678/5595.

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蔡承軒. "Gaussian Mixture of Model based Arm Gesture Recognition Research." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/01813447582385659631.

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碩士<br>國立臺灣師範大學<br>資訊工程研究所<br>100<br>Human body gesture recognition is one of the top research topic, and it had been developed for a long time. Due to its massive computational complexity and its expensive equipment, these system can’t be used by grassroots. In this paper, we develop a “Gaussian Mixture of Model based Arm Gesture Recognition Research” Real-time system, and using temporal differencing to get a moving object, under the hypothesis of knowing the range of upper body. These image then apply Gaussian Mixture of Model, using multiple Gaussian functions, to describe multiple backgrou
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Wang, Jhong-yi, and 王中義. "Speaker and Emotion Recognition System of Gaussian Mixture Model." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/69265552004018500336.

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碩士<br>國立中山大學<br>電機工程學系研究所<br>94<br>In this thesis, the speaker and emotion recognition system is established by PC and digit signal processor (DSP). Most speaker and emotion recognition systems are separately accomplished, but not combined together in the same system. In this thesis, it will show how speaker and emotion recognition systems are combined in the same system. In this system, the voice is picked up by a mike and through DSP to extract the characteristics. Then it passes the sample correctly, it can draw the result of distinguishing. The recognition system is divided into four sub-s
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Chang, Zhi-Jie, and 張智傑. "Language Identification based on Gaussian Mixture Model Tokenizer and Language Model." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/81926368859761798108.

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Li, Pengfei. "Hypothesis Testing in Finite Mixture Models." Thesis, 2007. http://hdl.handle.net/10012/3442.

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Mixture models provide a natural framework for unobserved heterogeneity in a population. They are widely applied in astronomy, biology, engineering, finance, genetics, medicine, social sciences, and other areas. An important first step for using mixture models is the test of homogeneity. Before one tries to fit a mixture model, it might be of value to know whether the data arise from a homogeneous or heterogeneous population. If the data are homogeneous, it is not even necessary to go into mixture modeling. The rejection of the homogeneous model may also have scientific implications. For exa
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