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Dissertations / Theses on the topic 'Gaussian Mixture Model'
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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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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 environmental noise are only weakly modelled and factorized by adaptation techniques such as maximum likelihood linear regression (MLLR), maximum a posteriori adaptation (MAP) and vocal tract length normalisation (VTLN). In this thesis, we will discuss an alternative acoustic modelling approach — the subspace Gaussian mixture model (SGMM), which is expected to deal with these two issues better. In an SGMM, the model parameters are derived from low-dimensional model and speaker subspaces that can capture phonetic and speaker correlations. Given these subspaces, only a small number of state-dependent parameters are required to derive the corresponding GMMs. Hence, the total number of model parameters can be reduced, which allows acoustic modelling with a limited amount of training data. In addition, the SGMM-based acoustic model factorizes the phonetic and speaker factors and within this framework, other source of acoustic variability may also be explored. In this thesis, we propose a regularised model estimation for SGMMs, which avoids overtraining in case that the training data is sparse. We will also take advantage of the structure of SGMMs to explore cross-lingual acoustic modelling for low-resource speech recognition. Here, the model subspace is estimated from out-domain data and ported to the target language system. In this case, only the state-dependent parameters need to be estimated which relaxes the requirement of the amount of training data. To improve the robustness of SGMMs against environmental noise, we propose to apply the joint uncertainty decoding (JUD) technique that is shown to be efficient and effective. We will report experimental results on the Wall Street Journal (WSJ) database and GlobalPhone corpora to evaluate the regularisation and cross-lingual modelling of SGMMs. Noise compensation using JUD for SGMM acoustic models is evaluated on the Aurora 4 database.
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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.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 operation gives the possibility of using diagonal covariances in modelling the data. Finally, a GMM based VQ is performed on the vectors.
In the decoder part the inverse operations are done. However, in order to prevent negative power spectrum elements due to inverse perceptual transformation in the decoder, instead of direct inversion, a Nonnegative Least Squares Algorithm has been used to switch back to frequency domain. For the sake of comparison, a reference subband based "Excitation Distortion coder" is implemented and comparing the resulting coded files showed a better performance for the proposed GMM based coder.
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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.Includes bibliographical references (leaves 71-72).
by Nikhil Sadarangani.
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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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 illustrates the effectiveness of adapting Gaussian mixture models in solving the treatment effect problem. We apply the mixture models - Gaussian Mixture Regression (GMR) and Gaussian Mixture Linear Regression (GMLR)- as a potentially simple and powerful tool to investigate the joint distribution of two potential outcomes. For GMR, we consider a joint distribution of the covariate and two potential outcomes. For GMLR, we consider a joint distribution of two potential outcomes, which linearly depend on covariate. Through developing an EM algorithm for GMLR, we find that GMR and GMLR are effective in estimating means and variances, but they are not effective in capturing correlation between two potential outcomes. In the second part of this thesis, GMLR is modified to capture unobserved covariance structure (correlation between outcomes) that can be explained by latent variables introduced through making an important model assumption. We propose a much more efficient Pre-Post EM Algorithm to implement our proposed GMLR model with unobserved covariance structure in practice. Simulation studies show that Pre-Post EM Algorithm performs well not only in estimating means and variances, but also in estimating covariance.
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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 mixture of regressions model. Two methods of calculating the coe cient of determination for measuring model performance are introduced. The application of nite mixtures of Gaussian distributions and regressions to image segmentation problems is examined. The traditional nite mixture models however, have a shortcoming in that commonality of location of observations (pixels) is not taken into account when clustering the data. In literature, this shortcoming is addressed by including a Markov random eld prior for the mixing probabilities and the present study discusses this theoretical development. The resulting nite spatial variant mixture of Gaussian regressions model is de ned and its application is demonstrated in a simulated example. It was found that the spatial variant mixture of Gaussian regressions delivered accurate spatial clustering results and simultaneously accurately estimated the component model parameters. This study contributes an application of the spatial variant mixture of Gaussian regressions model in the agricultural context: maize yields in the Free State are modelled as a function of precipitation, type of maize and season; GPS coordinates linked to the observations provide the location information. A simple linear regression and traditional mixture of Gaussian regressions model were tted for comparative purposes and the latter identi ed three distinct clusters without accounting for location information. It was found that the application of the spatial variant mixture of regressions model resulted in spatially distinct and informative clusters, especially with respect to the type of maize covariate. However, the estimated component regression models for this data set were quite similar. The investigated data set was not perfectly suited for the spatial variant mixture of regressions model application and possible solutions were proposed to improve the model results in future studies. A key learning from the present study is that the e ectiveness of the spatial variant mixture of regressions model is dependent on the clear and distinguishable spatial dependencies in the underlying data set when it is applied to map-type data.Dissertation (MSc)--University of Pretoria, 2017.
Statistics
MSc
Unrestricted
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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 of non-empty components visited during MCMC sampling. Specifying a shrinkage prior, namely the normal gamma prior, on the component means leads to improved parameter estimates as well as identification of cluster-relevant variables. After estimating the mixture model using MCMC methods based on data augmentation and Gibbs sampling, an identified model is obtained by relabeling the MCMC output in the point process representation of the draws. This is performed using K-centroids cluster analysis based on the Mahalanobis distance. We evaluate our proposed strategy in a simulation setup with artificial data and by applying it to benchmark data sets. (authors' abstract)
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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.First, a Greedy level allocation algorithm is developed based on the philosophy of the Greedy algorithin but, it does so level by level, considering the best benefit and bit cost yielded by an allocation. The Greedy level allocation algorithm is computationally intensive in general, thus we discuss combining it with other algorithms to obtain lower costs.
Second, another algorithm solving problems of negative bit allocations and integer level is proposed. The level allocations are to keep a certain ratio with respect to each other throughout the algorithm in order to remain closest to the condition for lowest distortion. Moreover, the original formula assumes a 6dB gain for each added bit, which is not generally true. The algorithm presents a new parameter k, which controls the benefit of adding one bit, usually set at 0.5 in the high-rate optimal bit allocation formula for MSE calling the new algorithm, the Two-Stage Iterative Bit Allocation (TSIBA) algorithm. Simulations show that modifying the bit allocation formula effectively brings about some gains over the previous methods.
The formula containing the new parameter is generalized into a, formula introducing a new parameter which weights not only the variances but also the dimensions, training the new parameter on their distribution function. The TSIBA was an a-posteriori decision algorithm, where the decision on which value of k to select for lowest distortion was decided after computing all distortions. The Generalized TSIBA (GTSIBA), on the other hand, uses a training procedure to estimate which weighting factor to set for each dimension at a certain bit rate. Simulation results show yet another improvement when using the Generalized TSIBA over all previous methods.
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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 blobs. Pixels are matched to the models using a fast numerical level set method. Since each target is tracked with its blob's information, the system is capable of handling partial or full occlusion during tracking. Experimental results on a number of challenging sequences that were collected in non-experimental environments demonstrate the effectiveness of the approach.
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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 for the proposed method and then it is tested against the threshold-based method. Segmentation times, dice coefficients, and volume errors are compared. The evaluation reveals that the proposed method has a comparable mean segmentation time to the threshold-based method, and performs faster in cases where the volume error does not exceed 40%. The mean dice coefficient and volume error are also improved while achieving lower deviation.
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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 different realizations of the same faults. In this study an algorithm is proposed that does not rely on labeled training data, instead the proposed algorithm clusters similar fault data together by combining an engine model and unsupervised learning in the form of a modified Gaussian mixture model using Expectation Maximization. If one or more of the fault scenarios in a cluster is later diagnosed, the rest of the data in the same cluster is likely to have the same diagnosis. The modified Gaussian mixture model proposed in this study takes into account that residual data, in some cases including the case in this study when the data is from an internal combustion engine, seem to diverge from the nominal case (data points near the origin) along a linear trajectory as the fault size increases. This is taken into account by modeling the clusters as Gaussian distributions around fault vectors that each represent the trajectories the data moves along as the fault size increases for each cluster or fault mode. The algorithm also takes into account that data from one scenario are likely to belong to the same fault class i.e. it is not necessary to classify each data point separately, instead the data can be clustered as batches. This study also evaluates the proposed model as a semi-supervised learner, where some data is known. In this case, the algorithm can also be used to estimate the fault sizes of unknown faults by using the acquired fault vectors, given that there are known fault sizes for other data in the same cluster. The algorithm is evaluated with data collected from an engine test bench using a commercial Volvo engine and shows promising results as most fault scenarios can be correctly clustered. However, results show that there are clustering ambiguities for data from small faults, as they are more similar to the nominal case and overlap more with data from other fault modes.
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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 analysis of neonatal health are early neonatal mortality, if the mother has HIV, anaemia, is a private patient and if the neonate is born after 36 completed weeks of gestation. It can be concluded that the mortality rates are high especially for low birthweights (2000 or less) in the estimated first and second components. However, due to wide confidence bounds it is hard to draw conclusions from the data.
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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 and Gaussian Mixture Models and secondly, using Hidden Markov Models and Deep Neural Networks. The later approach using deep neural networks is found to achieve a better performance in terms of Word Error Rate.De senaste åren har olika tillämpningar inom människa-dator interaktion och främst taligenkänning hittat sig ut på den allmänna marknaden. Många system och tekniska produkter stöder idag tjänsterna att transkribera tal och diktera text. Detta gäller dock främst de större språken och sällan finns samma stöd för mindre språk som exempelvis svenskan. I detta examensprojekt har en modell för taligenkänning på svenska ut- vecklas. Det är genomfört på uppdrag av Sveriges Radio som skulle ha stor nytta av en fungerande taligenkänningsmodell på svenska. Modellen är utvecklad i ramverket Kaldi. Två tillvägagångssätt för den akustiska träningen av modellen är implementerade och prestandan för dessa två är evaluerade och jämförda. Först tränas en modell med användningen av Hidden Markov Models och Gaussian Mixture Models och slutligen en modell där Hidden Markov Models och Deep Neural Networks an- vänds, det visar sig att den senare uppnår ett bättre resultat i form av måttet Word Error Rate.
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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-HMM) a été l'une des techniques les plus utilisées dans les systèmes de RAP pendant de nombreuses décennies. Plusieurs techniques d'adaptation ont été développées pour ce type de modèles. Les modèles acoustiques combinant HMM et DNN (DNN-HMM) ont récemment permis de grandes avancées et surpassé les modèles GMM-HMM pour diverses tâches de RAP, mais l'adaptation au locuteur reste très difficile pour les modèles DNN-HMM. L'objectif principal de cette thèse est de développer une méthode de transfert efficace des algorithmes d'adaptation des modèles GMM aux modèles DNN. Une nouvelle approche pour l'adaptation au locuteur des modèles acoustiques de type DNN est proposée et étudiée : elle s'appuie sur l'utilisation de fonctions dérivées de GMM comme entrée d'un DNN. La technique proposée fournit un cadre général pour le transfert des algorithmes d'adaptation développés pour les GMM à l'adaptation des DNN. Elle est étudiée pour différents systèmes de RAP à l'état de l'art et s'avère efficace par rapport à d'autres techniques d'adaptation au locuteur, ainsi que complémentaireDifferences between training and testing conditions may significantly degrade recognition accuracy in automatic speech recognition (ASR) systems. Adaptation is an efficient way to reduce the mismatch between models and data from a particular speaker or channel. There are two dominant types of acoustic models (AMs) used in ASR: Gaussian mixture models (GMMs) and deep neural networks (DNNs). The GMM hidden Markov model (GMM-HMM) approach has been one of the most common technique in ASR systems for many decades. Speaker adaptation is very effective for these AMs and various adaptation techniques have been developed for them. On the other hand, DNN-HMM AMs have recently achieved big advances and outperformed GMM-HMM models for various ASR tasks. However, speaker adaptation is still very challenging for these AMs. Many adaptation algorithms that work well for GMMs systems cannot be easily applied to DNNs because of the different nature of these models. The main purpose of this thesis is to develop a method for efficient transfer of adaptation algorithms from the GMM framework to DNN models. A novel approach for speaker adaptation of DNN AMs is proposed and investigated. The idea of this approach is based on using so-called GMM-derived features as input to a DNN. The proposed technique provides a general framework for transferring adaptation algorithms, developed for GMMs, to DNN adaptation. It is explored for various state-of-the-art ASR systems and is shown to be effective in comparison with other speaker adaptation techniques and complementary to them
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Kawaguchi, Nobuo, Katsuhiko Kaji, Susumu Fujita, 信夫 河口, 克彦 梶, and 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, and 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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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 security level of the speaker verification system. To demonstrate this, we analyze the YOHO database, a corpus of voice samples collected from 138 speakers and show that the amount of entropy extracted is less than 14-bits. We also present a practical attack that succeeds in impersonating the voice of any speaker within the corpus with a 98% success probability with as little as 9 trials. The attack will still succeed with a rate of 62.50% if 4 attempts are permitted. Further, based on the same attack rationale, we mount an attack on the ALIZE speaker verification system. We show through experimentation that the attacker can impersonate any user in the database of 69 people with about 25% success rate with only 5 trials. The success rate can achieve more than 50% by increasing the allowed authentication attempts to 20. Finally, when the practical attack is cast in terms of an entropy metric, we find that the theoretical entropy estimate almost perfectly predicts the success rate of the practical attack, giving further credence to the theoretical model and the associated entropy estimation technique.
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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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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 - PhDThe 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 well. We use mixtures of univariate Gaussian models and mixtures of regression models to investigate the existence of bimodality in the distribution of the widths of autocorrelation functions in a sample of 119 gamma-ray bursts. Contrary to previous findings, our results do not reveal any evidence of bimodality. We extend a study by Genest et al. (2012) of the power and significance levels of tests of copula symmetry, to two copula models which have not been considered previously. Our results confirm that for small sample sizes, these tests fail to maintain their 5% significance level and that the Cramer-von Mises-type statistics are the most powerful.
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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 thespeaker modeling. The back-end is based on the Gaussian Mixture Model (GMM) andGaussian Mixture Model-Universal Background Model (GMM-UBM) methods forenrollment and verification of the specific speaker. In addition, normalization techniquessuch as Cepstral Means Subtraction (CMS) and feature warping is also used forrobustness against noise and distortion. In this paper, we are going to build a speakerverification system and experiment with a variance in the amount of training data for thetrue speaker model, and to evaluate the system performance. And further investigate thearea of security in a speaker verification system then two methods are compared (GMMand GMM-UBM) to experiment on which is more secure depending on the amount oftraining data available.This research will therefore give a contribution to how much data is really necessary fora secure system where the False Positive is as close to zero as possible, how will theamount of training data affect the False Negative (FN), and how does this differ betweenGMM and GMM-UBM.The result shows that an increase in speaker specific training data will increase theperformance of the system. However, too much training data has been proven to beunnecessary because the performance of the system will eventually reach its highest point and in this case it was around 48 min of data, and the results also show that the GMMUBM model containing 48- to 60 minutes outperformed the GMM models.
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Zhang, Di. "INFORMATION THEORETIC CRITERIA FOR IMAGE QUALITY ASSESSMENT BASED ON NATURAL SCENE STATISTICS." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2842.
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Measurement of visual quality is crucial for various image and video processing applications. The goal of objective image quality assessment is to introduce a computational quality metric that can predict image or video quality. Many methods have been proposed in the past decades. Traditionally, measurements convert the spatial data into some other feature domains, such as the Fourier domain, and detect the similarity, such as mean square distance or Minkowsky distance, between the test data and the reference or perfect data, however only limited success has been achieved. None of the complicated metrics show any great advantage over other existing metrics.
The common idea shared among many proposed objective quality metrics is that human visual error sensitivities vary in different spatial and temporal frequency and directional channels. In this thesis, image quality assessment is approached by proposing a novel framework to compute the lost information in each channel not the similarities as used in previous methods. Based on natural scene statistics and several image models, an information theoretic framework is designed to compute the perceptual information contained in images and evaluate image quality in the form of entropy.
The thesis is organized as follows. Chapter I give a general introduction about previous work in this research area and a brief description of the human visual system. In Chapter II statistical models for natural scenes are reviewed. Chapter III proposes the core ideas about the computation of the perceptual information contained in the images. In Chapter IV, information theoretic criteria for image quality assessment are defined. Chapter V presents the simulation results in detail. In the last chapter, future direction and improvements of this research are discussed.
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Roy, Tamoghna. "BER Modeling for Interference Canceling Adaptive NLMS Equalizer." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/78055.
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Adaptive LMS equalizers are widely used in digital communication systems for their simplicity in implementation. Conventional adaptive filtering theory suggests the upper bound of the performance of such equalizer is determined by the performance of a Wiener filter of the same structure. However, in the presence of a narrowband interferer the performance of the LMS equalizer is better than that of its Wiener counterpart. This phenomenon, termed a non-Wiener effect, has been observed before and substantial work has been done in explaining the underlying reasons. In this work, we focus on the Bit Error Rate (BER) performance of LMS equalizers.
At first a model – the Gaussian Mixture (GM) model – is presented to estimate the BER performance of a Wiener filter operating in an environment dominated by a narrowband interferer. Simulation results show that the model predicts BER accurately for a wide range of SNR, ISR, and equalizer length. Next, a model similar to GM termed the Gaussian Mixture using Steady State Weights (GMSSW) model is proposed to model the BER behavior of the adaptive NLMS equalizer. Simulation results show unsatisfactory performance of the model. A detailed discussion is presented that points out the limitations of the GMSSW model, thereby providing some insight into the non-Wiener behavior of (N)LMS equalizers. An improved model, the Gaussian with Mean Square Error (GMSE), is then proposed. Simulation results show that the GMSE model is able to model the non-Wiener characteristics of the NLMS equalizer when the normalized step size is between 0 and 0.4. A brief discussion is provided on why the model is inaccurate for larger step sizes.Master of Science
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Zelenty, Jennifer Evelyn. "Effects of nickel and manganese on the embrittlement of low-copper pressure vessel steels." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:28b9151f-1644-470b-abc7-48ff82bcffdd.
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Solute clustering is known to play a significant role in the embrittlement of reactor pressure vessel (RPV) steels. When precipitates form they impede the movement of dislocations, causing an increase in hardness and a shift in the ductile-brittle transition temperature. Over time this can cause the steel to become brittle and more susceptible to fracture. Thus, understanding precipitate formation is of great importance to the nuclear industry. The first part of this thesis aims to isolate and better understand the thermal aging component of embrittlement in low copper, model RPV steels. Currently, relatively little is known about the effects of Ni and Mn in a low copper environment. Therefore, it is of interest to determine if Ni and Mn form precipitates under these conditions. To this end, hardness measurements and atom probe tomography were utilized to link the mechanical properties to the microstructure. After 11,690 hours of thermal aging a statistically significant decrease in hardening was observed. Consistent with hardness measurements, no precipitates were present within the matrix of the thermally aged RPV steels. The local chemistry method was then applied to investigate the very early stages of solute clustering. Association was found to be statistically significant in both the thermally aged and as-received model RPV steels. Therefore, no apparent trends regarding the changes in solute association between the as-received and thermally aged RPV steels were identified. Small, non-random clusters were observed at heterogeneous nucleation sites, such as carbide/matrix interfaces and grain boundaries, within the thermally aged material. The clusters found at the carbide/matrix interfaces were all rich in Mn and approximately 90-150 atoms in size. The clusters located along the observed low-angle grain boundary, however, were significantly larger (on the order of hundreds of atoms) and rich in Ni. Lastly, copper-rich precipitates (CRPs) and Mn- and Ni-rich precipitates (MNPs) were observed within the cementite phase of a high copper and low copper RPV steel, respectively, following long term thermal aging. APT was used to characterize these precipitates and obtain more detailed chemical information. The presence of such precipitates indicates that a range of precipitation can take place within the cementite phase of thermally aged RPV steels. The second part of this thesis aims to investigate the effects of ion irradiation on the microstructure of low copper RPV steels via APT. These steels were ion irradiated with 6.4 MeV Fe3+ ions with a dose rate of 1.5 x 10-4 dpa/s at 290°C. MNPs were observed in all five of the RPV steels analyzed. These precipitates were found to have nucleated within the matrix as well as at dislocations and grain boundaries. Using the maximum separation method these MNPs were extracted and characterized. Precipitate composition, size, volume fraction, and number density were determined for each of the five samples. Lastly, several grain boundaries were characterized. Several emerging trends were observed within the samples: Ni content within the precipitates did not vary significantly once a threshold between 30-50% was reached; bulk Mn content appeared to dictate Si and Mn content within the precipitates; and samples low in bulk Ni content were characterized by a higher number density of smaller precipitates. Additionally, by regressing precipitate volume fraction against the interaction of Ni and Mn, a linear relationship was found to be statistically significant.
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Zhang, Lin. "Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/95040.
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We are living in an era in which many mysteries related to science, technologies and design can be answered by "learning" the huge amount of data accumulated over the past few decades. In the processes of those endeavors, highly-correlated high-dimensional data are frequently observed in many areas including predicting shelf life, controlling manufacturing processes, and identifying important pathways related with diseases. We define a "set" as a group of highly-correlated high-dimensional (HCHD) variables that possess a certain practical meaning or control a certain process, and define an "element" as one of the HCHD variables within a certain set. Such an elements-within-a-set structure is very complicated because: (i) the dimensions of elements in different sets can vary dramatically, ranging from two to hundreds or even thousands; (ii) the true relationships, include element-wise associations, set-wise interactions, and element-set interactions, are unknown; (iii) and the sample size (n) is usually much smaller than the dimension of the elements (p). The goal of this dissertation is to provide a systematic way to identify both the set effects and the element effects associated with survival outcomes from heterogeneous populations using Bayesian survival kernel models. By connecting kernel machines with semiparametric Bayesian hierarchical models, the proposed unified model frameworks can identify significant elements as well as sets regardless of mis-specifications of distributions or kernels. The proposed methods can potentially be applied to a vast range of fields to solve real-world problems.PHD
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Sikora, Jan. "Statický model scény." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220128.
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This thesis deal with various methods of background detection and with it related motion detection in a scene. It's progressing from simplest methods to more comlex. For every one are reviewed the possibilities of using and her drawbacks. In introduction are described various types of scenes according to background and foreground type e.g . according to movement objects speed or presence of movement in background. Is proposed several common or specific improvements for obtaining better background even by using simple method. Next part of work solve real situation of shaking camera. There are tested two basic methods for optical stabilization. The first is registration of images by template matching. Alternative method used interest points (corners). Both methods are closely examinate and is sought best way to match following pictures. Except shaking of camera this work deal with rotating camera and in theory solve detection background from cameras placed on ridden car. Part of work is creation database of different types scenes
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Xu, Li. "Statistical Methods for Variability Management in High-Performance Computing." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104184.
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High-performance computing (HPC) variability management is an important topic in computer science. Research topics include experimental designs for efficient data collection, surrogate models for predicting the performance variability, and system configuration optimization. Due to the complex architecture of HPC systems, a comprehensive study of HPC variability needs large-scale datasets, and experimental design techniques are useful for improved data collection. Surrogate models are essential to understand the variability as a function of system parameters, which can be obtained by mathematical and statistical models. After predicting the variability, optimization tools are needed for future system designs.
This dissertation focuses on HPC input/output (I/O) variability through three main chapters. After the general introduction in Chapter 1, Chapter 2 focuses on the prediction models for the scalar description of I/O variability. A comprehensive comparison study is conducted, and major surrogate models for computer experiments are investigated. In addition, a tool is developed for system configuration optimization based on the chosen surrogate model.
Chapter 3 conducts a detailed study for the multimodal phenomena in I/O throughput distribution and proposes an uncertainty estimation method for the optimal number of runs for future experiments. Mixture models are used to identify the number of modes for throughput distributions at different configurations. This chapter also addresses the uncertainty in parameter estimation and derives a formula for sample size calculation. The developed method is then applied to HPC variability data.
Chapter 4 focuses on the prediction of functional outcomes with both qualitative and quantitative factors. Instead of a scalar description of I/O variability, the distribution of I/O throughput provides a comprehensive description of I/O variability. We develop a modified Gaussian process for functional prediction and apply the developed method to the large-scale HPC I/O variability data.
Chapter 5 contains some general conclusions and areas for future work.Doctor of Philosophy
This dissertation focuses on three projects that are all related to statistical methods in performance variability management in high-performance computing (HPC). HPC systems are computer systems that create high performance by aggregating a large number of computing units. The performance of HPC is measured by the throughput of a benchmark called the IOZone Filesystem Benchmark. The performance variability is the variation among throughputs when the system configuration is fixed. Variability management involves studying the relationship between performance variability and the system configuration. In Chapter 2, we use several existing prediction models to predict the standard deviation of throughputs given different system configurations and compare the accuracy of predictions. We also conduct HPC system optimization using the chosen prediction model as the objective function. In Chapter 3, we use the mixture model to determine the number of modes in the distribution of throughput under different system configurations. In addition, we develop a model to determine the number of additional runs for future benchmark experiments. In Chapter 4, we develop a statistical model that can predict the throughout distributions given the system configurations. We also compare the prediction of summary statistics of the throughput distributions with existing prediction models.
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Oliveira, Luan Soares. "Classificação de fluxos de dados não estacionários com algoritmos incrementais baseados no modelo de misturas gaussianas." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-06042016-143503/.
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Aprender conceitos provenientes de fluxos de dados é uma tarefa significamente diferente do aprendizado tradicional em lote. No aprendizado em lote, existe uma premissa implicita que os conceitos a serem aprendidos são estáticos e não evoluem significamente com o tempo. Por outro lado, em fluxos de dados os conceitos a serem aprendidos podem evoluir ao longo do tempo. Esta evolução é chamada de mudança de conceito, e torna a criação de um conjunto fixo de treinamento inaplicável neste cenário. O aprendizado incremental é uma abordagem promissora para trabalhar com fluxos de dados. Contudo, na presença de mudanças de conceito, conceitos desatualizados podem causar erros na classificação de eventos. Apesar de alguns métodos incrementais baseados no modelo de misturas gaussianas terem sido propostos na literatura, nota-se que tais algoritmos não possuem uma política explicita de descarte de conceitos obsoletos. Nesse trabalho um novo algoritmo incremental para fluxos de dados com mudanças de conceito baseado no modelo de misturas gaussianas é proposto. O método proposto é comparado com vários algoritmos amplamente utilizados na literatura, e os resultados mostram que o algoritmo proposto é competitivo com os demais em vários cenários, superando-os em alguns casos.Learning concepts from data streams differs significantly from traditional batch learning. In batch learning there is an implicit assumption that the concept to be learned is static and does not evolve significantly over time. On the other hand, in data stream learning the concepts to be learned may evolve over time. This evolution is called concept drift, and makes the creation of a fixed training set be no longer applicable. Incremental learning paradigm is a promising approach for learning in a data stream setting. However, in the presence of concept drifts, out dated concepts can cause misclassifications. Several incremental Gaussian mixture models methods have been proposed in the literature, but these algorithms lack an explicit policy to discard outdated concepts. In this work, a new incremental algorithm for data stream with concept drifts based on Gaussian Mixture Models is proposed. The proposed methodis compared to various algorithms widely used in the literature, and the results show that it is competitive with them invarious scenarios, overcoming them in some cases.
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Wu, Jingwen. "Model-based clustering and model selection for binned data." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0005/document.
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Cette thèse étudie les approches de classification automatique basées sur les modèles de mélange gaussiens et les critères de choix de modèles pour la classification automatique de données discrétisées. Quatorze algorithmes binned-EM et quatorze algorithmes bin-EM-CEM sont développés pour quatorze modèles de mélange gaussiens parcimonieux. Ces nouveaux algorithmes combinent les avantages des données discrétisées en termes de réduction du temps d’exécution et les avantages des modèles de mélange gaussiens parcimonieux en termes de simplification de l'estimation des paramètres. Les complexités des algorithmes binned-EM et bin-EM-CEM sont calculées et comparées aux complexités des algorithmes EM et CEM respectivement. Afin de choisir le bon modèle qui s'adapte bien aux données et qui satisfait les exigences de précision en classification avec un temps de calcul raisonnable, les critères AIC, BIC, ICL, NEC et AWE sont étendus à la classification automatique de données discrétisées lorsque l'on utilise les algorithmes binned-EM et bin-EM-CEM proposés. Les avantages des différentes méthodes proposées sont illustrés par des études expérimentalesThis thesis studies the Gaussian mixture model-based clustering approaches and the criteria of model selection for binned data clustering. Fourteen binned-EM algorithms and fourteen bin-EM-CEM algorithms are developed for fourteen parsimonious Gaussian mixture models. These new algorithms combine the advantages in computation time reduction of binning data and the advantages in parameters estimation simplification of parsimonious Gaussian mixture models. The complexities of the binned-EM and the bin-EM-CEM algorithms are calculated and compared to the complexities of the EM and the CEM algorithms respectively. In order to select the right model which fits well the data and satisfies the clustering precision requirements with a reasonable computation time, AIC, BIC, ICL, NEC, and AWE criteria, are extended to binned data clustering when the proposed binned-EM and bin-EM-CEM algorithms are used. The advantages of the different proposed methods are illustrated through experimental studies
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Mangayyagari, Srikanth. "Voice recognition system based on intra-modal fusion and accent classification." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002229.
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Andrésen, Anton, and Adam Håkansson. "Comparing unsupervised clustering algorithms to locate uncommon user behavior in public travel data : A comparison between the K-Means and Gaussian Mixture Model algorithms." Thesis, Tekniska Högskolan, Jönköping University, JTH, Datateknik och informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-49243.
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Clustering machine learning algorithms have existed for a long time and there are a multitude of variations of them available to implement. Each of them has its advantages and disadvantages, which makes it challenging to select one for a particular problem and application. This study focuses on comparing two algorithms, the K-Means and Gaussian Mixture Model algorithms for outlier detection within public travel data from the travel planning mobile application MobiTime1[1]. The purpose of this study was to compare the two algorithms against each other, to identify differences between their outlier detection results. The comparisons were mainly done by comparing the differences in number of outliers located for each model, with respect to outlier threshold and number of clusters. The study found that the algorithms have large differences regarding their capabilities of detecting outliers. These differences heavily depend on the type of data that is used, but one major difference that was found was that K-Means was more restrictive then Gaussian Mixture Model when it comes to classifying data points as outliers. The result of this study could help people determining which algorithms to implement for their specific application and use case.
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Palmer, Jason Allan. "Variational and scale mixture representations of non-Gaussian densities for estimation in the Bayesian Linear Model sparse coding, independent component analysis, and minimum entropy segmentation /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3237562.
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Thesis (Ph. D.)--University of California, San Diego, 2006.Title from first page of PDF file (viewed December 13, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 143-150).
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Xiao, Ying. "New tools for unsupervised learning." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52995.
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In an unsupervised learning problem, one is given an unlabelled dataset and hopes to find some hidden structure; the prototypical example is clustering similar data. Such problems often arise in machine learning and statistics, but also in signal processing, theoretical computer science, and any number of quantitative scientific fields. The distinguishing feature of unsupervised learning is that there are no privileged variables or labels which are particularly informative, and thus the greatest challenge is often to differentiate between what is relevant or irrelevant in any particular dataset or problem.
In the course of this thesis, we study a number of problems which span the breadth of unsupervised learning. We make progress in Gaussian mixtures, independent component analysis (where we solve the open problem of underdetermined ICA), and we formulate and solve a feature selection/dimension reduction model. Throughout, our goal is to give finite sample complexity bounds for our algorithms -- these are essentially the strongest type of quantitative bound that one can prove for such algorithms. Some of our algorithmic techniques turn out to be very efficient in practice as well.
Our major technical tool is tensor spectral decomposition: tensors are generalisations of matrices, and often allow access to the "fine structure" of data. Thus, they are often the right tools for unravelling the hidden structure in an unsupervised learning setting. However, naive generalisations of matrix algorithms to tensors run into NP-hardness results almost immediately, and thus to solve our problems, we are obliged to develop two new tensor decompositions (with robust analyses) from scratch. Both of these decompositions are polynomial time, and can be viewed as efficient generalisations of PCA extended to tensors.
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Raman, Pujita. "Speaker Identification and Verification Using Line Spectral Frequencies." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/52964.
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State-of-the-art speaker identification and verification (SIV) systems provide near perfect performance under clean conditions. However, their performance deteriorates in the presence of background noise. Many feature compensation, model compensation and signal enhancement techniques have been proposed to improve the noise-robustness of SIV systems. Most of these techniques require extensive training, are computationally expensive or make assumptions about the noise characteristics. There has not been much focus on analyzing the relative importance, or speaker-discriminative power of different speech zones, particularly under noisy conditions.
In this work, an automatic, text-independent speaker identification (SI) system and speaker verification (SV) system is proposed using Line Spectral Frequency (LSF) features. The performance of the proposed SI and SV systems are evaluated under various types of background noise. A score-level fusion based technique is implemented to extract complementary information from static and dynamic LSF features. The proposed score-level fusion based SI and SV systems are found to be more robust under noisy conditions.
In addition, we investigate the speaker-discriminative power of different speech zones such as vowels, non-vowels and transitions. Rapidly varying regions of speech such as consonant-vowel transitions are found to be most speaker-discriminative in high SNR conditions. Steady, high-energy vowel regions are robust against noise and are hence most speaker-discriminative in low SNR conditions. We show that selectively utilizing features from a combination of transition and steady vowel zones further improves the performance of the score-level fusion based SI and SV systems under noisy conditions.Master of Science
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Miyajima, Chiyomi, Yoshihiro Nishiwaki, Koji Ozawa, Toshihiro Wakita, Katsunobu Itou, Kazuya Takeda, and Fumitada Itakura. "Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification." IEEE, 2007. http://hdl.handle.net/2237/9623.
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Jia, Jia. "Interactive Imaging via Hand Gesture Recognition." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4259.
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With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. As a sub-field, Interactive Image Processing emphasizes particularly on the communications between machine and human. The basic flowchart is definition of object, analysis and training phase, recognition and feedback. Generally speaking, the core issue is how we define the interesting object and track them more accurately in order to complete the interaction process successfully.
This thesis proposes a novel dynamic simulation scheme for interactive image processing. The work consists of two main parts: Hand Motion Detection and Hand Gesture recognition. Within a hand motion detection processing, movement of hand will be identified and extracted. In a specific detection period, the current image is compared with the previous image in order to generate the difference between them. If the generated difference exceeds predefined threshold alarm, a typical hand motion movement is detected. Furthermore, in some particular situations, changes of hand gesture are also desired to be detected and classified. This task requires features extraction and feature comparison among each type of gestures. The essentials of hand gesture are including some low level features such as color, shape etc. Another important feature is orientation histogram. Each type of hand gestures has its particular representation in the domain of orientation histogram. Because Gaussian Mixture Model has great advantages to represent the object with essential feature elements and the Expectation-Maximization is the efficient procedure to compute the maximum likelihood between testing images and predefined standard sample of each different gesture, the comparability between testing image and samples of each type of gestures will be estimated by Expectation-Maximization algorithm in Gaussian Mixture Model. The performance of this approach in experiments shows the proposed method works well and accurately.
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Busch, Andrew W. "Wavelet transform for texture analysis with application to document analysis." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15908/1/Andrew_Busch_Thesis.pdf.
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Texture analysis is an important problem in machine vision, with applications in many fields including medical imaging, remote sensing (SAR), automated flaw detection in various products, and document analysis to name but a few. Over the last four decades many techniques for the analysis of textured images have been proposed in the literature for the purposes of classification, segmentation, synthesis and compression. Such approaches include analysis the properties of individual texture elements, using statistical features obtained from the grey-level values of the image itself, random field models, and multichannel filtering. The wavelet transform, a unified framework for the multiresolution decomposition of signals, falls into this final category, and allows a texture to be examined in a number of resolutions whilst maintaining spatial resolution.
This thesis explores the use of the wavelet transform to the specific task of texture classification and proposes a number of improvements to existing techniques, both in the area of feature extraction and classifier design. By applying a nonlinear transform to the wavelet coefficients, a better characterisation can be obtained for many natural textures, leading to increased classification performance when using first and second order statistics of these coefficients as features. In the area of classifier design, a combination of an optimal discriminate function and a non-parametric Gaussian mixture model classifier is shown to experimentally outperform other classifier configurations.
By modelling the relationships between neighbouring bands of the wavelet trans- form, more information regarding a texture can be obtained. Using such a representation, an efficient algorithm for the searching and retrieval of textured images from a database is proposed, as well as a novel set of features for texture classification. These features are experimentally shown to outperform features proposed in the literature, as well as provide increased robustness to small changes in scale. Determining the script and language of a printed document is an important task in the field of document processing. In the final part of this thesis, the use of texture analysis techniques to accomplish these tasks is investigated. Using maximum a posterior (MAP) adaptation, prior information regarding the nature of script images can be used to increase the accuracy of these methods. Novel techniques for estimating the skew of such documents, normalising text block prior to extraction of texture features and accurately classifying multiple fonts are also presented.
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Busch, Andrew W. "Wavelet Transform For Texture Analysis With Application To Document Analysis." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15908/.
Full textAbstract:
Texture analysis is an important problem in machine vision, with applications in many fields including medical imaging, remote sensing (SAR), automated flaw detection in various products, and document analysis to name but a few. Over the last four decades many techniques for the analysis of textured images have been proposed in the literature for the purposes of classification, segmentation, synthesis and compression. Such approaches include analysis the properties of individual texture elements, using statistical features obtained from the grey-level values of the image itself, random field models, and multichannel filtering. The wavelet transform, a unified framework for the multiresolution decomposition of signals, falls into this final category, and allows a texture to be examined in a number of resolutions whilst maintaining spatial resolution. This thesis explores the use of the wavelet transform to the specific task of texture classification and proposes a number of improvements to existing techniques, both in the area of feature extraction and classifier design. By applying a nonlinear transform to the wavelet coefficients, a better characterisation can be obtained for many natural textures, leading to increased classification performance when using first and second order statistics of these coefficients as features. In the area of classifier design, a combination of an optimal discriminate function and a non-parametric Gaussian mixture model classifier is shown to experimentally outperform other classifier configurations. By modelling the relationships between neighbouring bands of the wavelet trans- form, more information regarding a texture can be obtained. Using such a representation, an efficient algorithm for the searching and retrieval of textured images from a database is proposed, as well as a novel set of features for texture classification. These features are experimentally shown to outperform features proposed in the literature, as well as provide increased robustness to small changes in scale. Determining the script and language of a printed document is an important task in the field of document processing. In the final part of this thesis, the use of texture analysis techniques to accomplish these tasks is investigated. Using maximum a posterior (MAP) adaptation, prior information regarding the nature of script images can be used to increase the accuracy of these methods. Novel techniques for estimating the skew of such documents, normalising text block prior to extraction of texture features and accurately classifying multiple fonts are also presented.
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Lidija, Krstanović. "Mera sličnosti između modela Gausovih smeša zasnovana na transformaciji prostora parametara." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=104904&source=NDLTD&language=en.
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Predmet istraživanja ovog rada je istraživanje i eksploatacija mogućnosti da parametri Gausovih komponenti korišćenih Gaussian mixture modela (GMM) aproksimativno leže na niže dimenzionalnoj površi umetnutoj u konusu pozitivno definitnih matrica. U tu svrhu uvodimo novu, mnogo efikasniju meru sličnosti između GMM-ova projektovanjem LPP-tipa parametara komponenti iz više dimenzionalnog parametarskog originalno konfiguracijskog prostora u prostor značajno niže dimenzionalnosti. Prema tome, nalaženje distance između dva GMM-a iz originalnog prostora se redukuje na nalaženje distance između dva skupa niže dimenzionalnih euklidskih vektora, ponderisanih odgovarajućim težinama. Predložena mera je pogodna za primene koje zahtevaju visoko dimenzionalni prostor obeležja i/ili veliki ukupan broj Gausovih komponenti. Razrađena metodologija je primenjena kako na sintetičkim tako i na realnim eksperimentalnim podacima.This thesis studies the possibility that the parameters of Gaussian components of aparticular Gaussian Mixture Model (GMM) lie approximately on a lower-dimensionalsurface embedded in the cone of positive definite matrices. For that case, we delivernovel, more efficient similarity measure between GMMs, by LPP-like projecting thecomponents of a particular GMM, from the high dimensional original parameter space,to a much lower dimensional space. Thus, finding the distance between two GMMs inthe original space is reduced to finding the distance between sets of lowerdimensional euclidian vectors, pondered by corresponding weights. The proposedmeasure is suitable for applications that utilize high dimensional feature spaces and/orlarge overall number of Gaussian components. We confirm our results on artificial, aswell as real experimental data.
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Jose, Neenu. "SPEAKER AND GENDER IDENTIFICATION USING BIOACOUSTIC DATA SETS." UKnowledge, 2018. https://uknowledge.uky.edu/ece_etds/120.
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Acoustic analysis of animal vocalizations has been widely used to identify the presence of individual species, classify vocalizations, identify individuals, and determine gender. In this work automatic identification of speaker and gender of mice from ultrasonic vocalizations and speaker identification of meerkats from their Close calls is investigated. Feature extraction was implemented using Greenwood Function Cepstral Coefficients (GFCC), designed exclusively for extracting features from animal vocalizations. Mice ultrasonic vocalizations were analyzed using Gaussian Mixture Models (GMM) which yielded an accuracy of 78.3% for speaker identification and 93.2% for gender identification. Meerkat speaker identification with Close calls was implemented using Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM), with an accuracy of 90.8% and 94.4% respectively. The results obtained shows these methods indicate the presence of gender and identity information in vocalizations and support the possibility of robust gender identification and individual identification using bioacoustic data sets.
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Bjarnason, Brynjar Smári. "Clustering metagenome contigs using coverage with CONCOCT." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208944.
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Metagenomics allows studying genetic potentials of microorganisms without prior cultivation. Since metagenome assembly results in fragmented genomes, a key challenge is to cluster the genome fragments (contigs) into more or less complete genomes. The goal of this project was to investigate how well CONCOCT bins assembled contigs into taxonomically relevant clusters using the abundance profiles of the contigs over multiple samples. This was done by studying the effects of different parameter settings for CONCOCT on the clustering results when clustering metagenome contigs from in silico model communities generated by mixing data from isolate genomes. These parameters control how the model that CONCOCT trains is tuned and then how the model fits contigs to their cluster. Each parameter was tested in isolation while others were kept at their default values. For each of the data set used, the number of clusters was kept constant at the known number of species and strains in their respective data set. The resulting configuration was to use a tied covariance model, using principal components explaining 90% of the variance, and filtering out contigs shorter than 3000 bp. It also suggested that all available samples should be used for the abundance profiles. Using these parameters for CONCOCT, it was executed to have it estimate the number of clusters automatically. This gave poor results which lead to the conclusion that the process for selecting the number of clusters that was implemented in CONCOCT, “Bayesian Information Criterion”, was not good enough. That led to the testing of another similar mathematical model, “Dirichlet Process Gaussian Mixture Model”, that uses a different algorithm to estimate number of clusters. This new model gave much better results and CONCOCT has adapted a similar model in later versions.Metagenomik möjliggör analys av arvsmassor i mikrobiella floror utan att först behöva odla mikroorgansimerna. Metoden innebär att man läser korta DNA-snuttar som sedan pusslas ihop till längre genomfragment (kontiger). Genom att gruppera kontiger som härstammar från samma organism kan man sedan återskapa mer eller mindre fullständiga genom, men detta är en svår bioinformatisk utmaning. Målsättningen med det här projektet var att utvärdera precisionen med vilken mjukvaran CONCOCT, som vi nyligen utvecklat, grupperar kontiger som härstammar från samma organism baserat på information om kontigernas sekvenskomposition och abundansprofil över olika prover. Vi testade hur olika parametrar påverkade klustringen av kontiger i artificiella metagenomdataset av olika komplexitet som vi skapade in silico genom att blanda data från tidigare sekvenserade genom. Parametrarna som testades rörde indata såväl som den statistiska modell som CONCOCT använder för att utföra klustringen. Parametrarna varierades en i taget medan de andra parametrarna hölls konstanta. Antalet kluster hölls också konstant och motsvarade antalet olika organismer i flororna. Bäst resultat erhölls då vi använde en låst kovariansmodell och använde principalkomponenter som förklarade 90% av variansen, samt filtrerade bort kontiger som var kortare än 3000 baspar. Vi fick också bäst resultat då vi använde alla tillgängliga prover. Därefter använde vi dessa parameterinställningar och lät CONCOCT själv bestämma lämpligt antal kluster i dataseten med “Bayesian Information Criterion” - metoden som då var implementerad i CONCOCT. Detta gav otillfredsställande resultat med i regel för få och för stora kluster. Därför testade vi en alternativ metod, “Dirichlet Process Gaussian Mixture Model”, för att uppskatta antal kluster. Denna metod gav avsevärt bättre resultat och i senare versioner av CONCOCT har en liknande metod implementerats.
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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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碩士國立臺灣師範大學
資訊工程研究所
98
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 adaptive Gaussian mixture model to describe the background of a scene. Based on this preliminary background model, we extract foreground objects and their accompanying shadows. Shadows are next separated from foreground objects through a series of intensity and color analyses. The characteristics of shadows are finally determined with the principal component analysis method and are embedded as an additional Gaussian in the background model. Experimental results demonstrated the feasibility of the proposed background model. Keywords: Dynamic scene, Adaptive Gaussian Mixture Model, Foreground detection, Shadow detection
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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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碩士清雲科技大學
電子工程所
100
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 to identify the variance of the fingers. In this paper the creation of sign language image of slash encoding, Department of the advantages of slash encoding the difference between your fingers the number of changes, and the Gaussian mixture model (GMM) to establish the model of sign language and identification. Such as poor recognition rate is adjusted probability distribution of weight values to improve the recognition rate. The entire the paper Shushing is the Gaussian mixture model (GMM), slash code, adjust the probability distribution of the weight value. Finally, after adjusting the probability distribution of weight values, we learned from the conclusion that the overall recognition results rose to 98.33%from 92.66% of the original, so changing the probability distribution of the weight value can effectively improve the recognition rate.
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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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碩士中華大學
資訊工程學系(所)
96
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 the feature vectors from the same bird species into several groups with each group represented by a Gaussian distribution. The self-splitting Gaussian mixture learning (SGML) algorithm is then employed to find an appropriate number of Gaussian components for each GMM. In addition, a model selection algorithm based on the Bayesian information criterion (BIC) is applied to select the optimal model between GMM and extended VQ (EVQ) according to the amount of training data available. Linear discriminant analysis (LDA) is finally exploited to increase the classification accuracy at a lower dimensional feature vector space. In our experiments, the combination of TDMFCC, DTDMFCC, and NASE modulation coefficients achieve the average classification accuracy of 83.9% for the classification of 28 bird species.
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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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碩士中央警察大學
刑事警察研究所
104
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 recognition has its own independent Gaussian probability density function. Since the process without iteration, it can significantly reduce the amount of calculation. And the speaker verification results show that it can still maintain a good recognition results. In this paper, we use Mel frequency cepstral coefficients(MFCCs) as the voice characteristic for speaker verification. The average error rate for speaker verification on Gaussian mixture model is 0.5901%, while it on modified Gaussian mixture model is 1.6700%, the gap between them was 1.0799%. The error rate of two methods during the speaker verification has less difference. But in the speaker model build process, modified Gaussian mixture model does not need to go through an iterative calculation. The calculation ways and time are more simple and faster than Gaussian mixture model. It can also be another consideration of algorithm for more speed and convenience.
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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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碩士義守大學
資訊工程學系碩士班
98
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 mistake the background as the foreground. However, the Gaussian mixture model uses statistical method to generate the background images, so it could use the statistical method to distinguish the changes of the light source as well as slight movements. Therefore, the background objects will not be regarded as the foreground objects. Exponential smoothing is based on time series with stability or regularity, therefore the time series can be reasonably extended. Exponential smoothing needs more complete historical information to accurately and objectively predict the future trend, followed by the forecast conclusion. The study used a static camera, so the image information will not change violently. The stationary background image displayed in each frames will be relevant with previous frames; as a result, exponential smoothing method can be used in establishing background images. The exponential smoothing for Gaussian mixture model proposed in this study is to use the characteristics of exponential smoothing to predict the future trends. This method can effectively construct the background image when there are many moving objects. Also, the computation is efficient in comparison to the traditional Gaussian mixture model method.
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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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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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