Relevant bibliographies by topics / Gaussian mixer model (GMM)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Gaussian mixer model (GMM)'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Gaussian mixer model (GMM)"

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Zhang, Lianjun, Chuangmin Liu, and Craig J. Davis. "A mixture model-based approach to the classification of ecological habitats using Forest Inventory and Analysis data." Canadian Journal of Forest Research 34, no. 5 (2004): 1150–56. http://dx.doi.org/10.1139/x04-005.

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A Gaussian mixture model (GMM) is used to classify Forest Inventory and Analysis (FIA) plots into six ecological habitats in the northeastern USA. The GMM approach captures intra-class variation by modeling each habitat class as a mixture of subclasses of Gaussian distributions. The classification is achieved based on the appropriate posterior probability. The GMM classifier outperforms a traditional statistical method (i.e., linear discriminant analysis or LDA), and produces similar overall accuracy rates to a commonly used neural network model (i.e., multi-layer perceptrons or MLP). For the classifications of individual ecological habitats, however, MLP produces better (or same) producers' classification accuracies for five of the six ecological habitats than does GMM. But the GMM's accuracy rates are more consistent (92%–97%) across the six ecological habitats than those of the MLP model (82%–99%). This study shows that GMM offers an attractive alternative for modeling the complex stand structure and relationships between variables in mixed-species forest stands.
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Jin, Qiwen, Yong Ma, Erting Pan, et al. "Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity." Remote Sensing 11, no. 20 (2019): 2434. http://dx.doi.org/10.3390/rs11202434.

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In recent years, endmember variability has received much attention in the field of hyperspectral unmixing. To solve the problem caused by the inaccuracy of the endmember signature, the endmembers are usually modeled to assume followed by a statistical distribution. However, those distribution-based methods only use the spectral information alone and do not fully exploit the possible local spatial correlation. When the pixels lie on the inhomogeneous region, the abundances of the neighboring pixels will not share the same prior constraints. Thus, in this paper, to achieve better abundance estimation performance, a method based on the Gaussian mixture model (GMM) and spatial group sparsity constraint is proposed. To fully exploit the group structure, we take the superpixel segmentation (SS) as preprocessing to generate the spatial groups. Then, we use GMM to model the endmember distribution, incorporating the spatial group sparsity as a mixed-norm regularization into the objective function. Finally, under the Bayesian framework, the conditional density function leads to a standard maximum a posteriori (MAP) problem, which can be solved using generalized expectation-maximization (GEM). Experiments on simulated and real hyperspectral data demonstrate that the proposed algorithm has higher unmixing precision compared with other state-of-the-art methods.
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Nagesh, A. "New Feature Vectors using GFCC for Speaker Identification." International Journal of Emerging Research in Management and Technology 6, no. 8 (2018): 243. http://dx.doi.org/10.23956/ijermt.v6i8.146.

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The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system. The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies have shown very good robustness against noise and acoustic change. The main idea is GFCC features based on GMM feature extraction is to improve the overall speaker identification performance in low signal to noise ratio (SNR) conditions.
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Wang, Dong, Xiaodong Wang, and Shaohe Lv. "An Overview of End-to-End Automatic Speech Recognition." Symmetry 11, no. 8 (2019): 1018. http://dx.doi.org/10.3390/sym11081018.

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Automatic speech recognition, especially large vocabulary continuous speech recognition, is an important issue in the field of machine learning. For a long time, the hidden Markov model (HMM)-Gaussian mixed model (GMM) has been the mainstream speech recognition framework. But recently, HMM-deep neural network (DNN) model and the end-to-end model using deep learning has achieved performance beyond HMM-GMM. Both using deep learning techniques,
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Satyanand, Singh, and Singh Pragya. "High level speaker specific features modeling in automatic speaker recognition system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1859–67. https://doi.org/10.11591/ijece.v10i2.pp1859-1867.

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Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.
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Wang, Ying. "Solving Multi-Instance Visual Scene Recognition with Classifier Ensemble Based on Unsupervised Clustering." Applied Mechanics and Materials 415 (September 2013): 338–44. http://dx.doi.org/10.4028/www.scientific.net/amm.415.338.

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This paper proposes a new image Multi-Instance (MI) bag generating method, which models an image with a Gaussian Mixed Model (GMM). The generated GMM is treated as an MI bag, of which the color and locally stable invariant components (SIFT) are the instances. Agglomerative Information Bottleneck clustering is employed to transform the MIL problem into single-instance learning problem so that single-instance classifiers can be used for classification. Finally, ensemble learningis involved to further enhance classifiers generalization ability. Experimental results demonstrate that the performance of the proposed framework for image recognition is superior to some common MI algorithms on average in a 5-category scene recognition task Key words:Multi-Instance Learning; Gaussian Mixed Model; AIB Clustering; image modeling; Single-Instance Bag; Ensemble Classifier; Scene Recognition
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Sun, Qi, Liwen Jiang, and Haitao Xu. "Expectation-Maximization Algorithm of Gaussian Mixture Model for Vehicle-Commodity Matching in Logistics Supply Chain." Complexity 2021 (January 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/9305890.

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A vehicle-commodity matching problem (VCMP) is presented for service providers to reduce the cost of the logistics system. The vehicle classification model is built as a Gaussian mixture model (GMM), and the expectation-maximization (EM) algorithm is designed to solve the parameter estimation of GMM. A nonlinear mixed-integer programming model is constructed to minimize the total cost of VCMP. The matching process between vehicle and commodity is realized by GMM-EM, as a preprocessing of the solution. The design of the vehicle-commodity matching platform for VCMP is designed to reduce and eliminate the information asymmetry between supply and demand so that the order allocation can work at the right time and the right place and use the optimal solution of vehicle-commodity matching. Furthermore, the numerical experiment of an e-commerce supply chain proves that a hybrid evolutionary algorithm (HEA) is superior to the traditional method, which provides a decision-making reference for e-commerce VCMP.
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Bandi, Hari, Dimitris Bertsimas, and Rahul Mazumder. "Learning a Mixture of Gaussians via Mixed-Integer Optimization." INFORMS Journal on Optimization 1, no. 3 (2019): 221–40. http://dx.doi.org/10.1287/ijoo.2018.0009.

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We consider the problem of estimating the parameters of a multivariate Gaussian mixture model (GMM) given access to n samples that are believed to have come from a mixture of multiple subpopulations. State-of-the-art algorithms used to recover these parameters use heuristics to either maximize the log-likelihood of the sample or try to fit first few moments of the GMM to the sample moments. In contrast, we present here a novel mixed-integer optimization (MIO) formulation that optimally recovers the parameters of the GMM by minimizing a discrepancy measure (either the Kolmogorov–Smirnov or the total variation distance) between the empirical distribution function and the distribution function of the GMM whenever the mixture component weights are known. We also present an algorithm for multidimensional data that optimally recovers corresponding means and covariance matrices. We show that the MIO approaches are practically solvable for data sets with n in the tens of thousands in minutes and achieve an average improvement of 60%–70% and 50%–60% on mean absolute percentage error in estimating the means and the covariance matrices, respectively, over the expectation–maximization (EM) algorithm independent of the sample size n. As the separation of the Gaussians decreases and, correspondingly, the problem becomes more difficult, the edge in performance in favor of the MIO methods widens. Finally, we also show that the MIO methods outperform the EM algorithm with an average improvement of 4%–5% on the out-of-sample accuracy for real-world data sets.
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Chen, Gang, Binjie Hou, and Tiangang Lei. "A new Monte Carlo sampling method based on Gaussian Mixture Model for imbalanced data classification." Mathematical Biosciences and Engineering 20, no. 10 (2023): 17866–85. http://dx.doi.org/10.3934/mbe.2023794.

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<abstract><p>Imbalanced data classification has been a major topic in the machine learning community. Different approaches can be taken to solve the issue in recent years, and researchers have given a lot of attention to data level techniques and algorithm level. However, existing methods often generate samples in specific regions without considering the complexity of imbalanced distributions. This can lead to learning models overemphasizing certain difficult factors in the minority data. In this paper, a Monte Carlo sampling algorithm based on Gaussian Mixture Model (MCS-GMM) is proposed. In MCS-GMM, we utilize the Gaussian mixed model to fit the distribution of the imbalanced data and apply the Monte Carlo algorithm to generate new data. Then, in order to reduce the impact of data overlap, the three sigma rule is used to divide data into four types, and the weight of each minority class instance based on its neighbor and probability density function. Based on experiments conducted on Knowledge Extraction based on Evolutionary Learning datasets, our method has been proven to be effective and outperforms existing approaches such as Synthetic Minority Over-sampling TEchnique.</p></abstract>
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Deng, Lei, and Yong Gao. "Gammachirp Filter Banks Applied in Roust Speaker Recognition Based GMM-UBM Classifier." International Arab Journal of Information Technology 17, no. 2 (2019): 170–77. http://dx.doi.org/10.34028/iajit/17/2/4.

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In this paper, authors propose an auditory feature extraction algorithm in order to improve the performance of the speaker recognition system in noisy environments. In this auditory feature extraction algorithm, the Gammachirp filter bank is adapted to simulate the auditory model of human cochlea. In addition, the following three techniques are applied: cube-root compression method, Relative Spectral Filtering Technique (RASTA), and Cepstral Mean and Variance Normalization algorithm (CMVN).Subsequently, based on the theory of Gaussian Mixes Model-Universal Background Model (GMM-UBM), the simulated experiment was conducted. The experimental results implied that speaker recognition systems with the new auditory feature has better robustness and recognition performance compared to Mel-Frequency Cepstral Coefficients(MFCC), Relative Spectral-Perceptual Linear Predictive (RASTA-PLP),Cochlear Filter Cepstral Coefficients (CFCC) and gammatone Frequency Cepstral Coefficeints (GFCC)
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Dissertations / Theses on the topic "Gaussian mixer model (GMM)"

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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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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 complémentaire<br>Differences 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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Miyajima, Chiyomi, Yoshihiro Nishiwaki, Koji Ozawa, et al. "Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification." IEEE, 2007. http://hdl.handle.net/2237/9623.

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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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Huang, Xuan. "Balance-guaranteed optimized tree with reject option for live fish recognition." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9779.

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This thesis investigates the computer vision application of live fish recognition, which is needed in application scenarios where manual annotation is too expensive, when there are too many underwater videos. This system can assist ecological surveillance research, e.g. computing fish population statistics in the open sea. Some pre-processing procedures are employed to improve the recognition accuracy, and then 69 types of features are extracted. These features are a combination of colour, shape and texture properties in different parts of the fish such as tail/head/top/bottom, as well as the whole fish. Then, we present a novel Balance-Guaranteed Optimized Tree with Reject option (BGOTR) for live fish recognition. It improves the normal hierarchical method by arranging more accurate classifications at a higher level and keeping the hierarchical tree balanced. BGOTR is automatically constructed based on inter-class similarities. We apply a Gaussian Mixture Model (GMM) and Bayes rule as a reject option after the hierarchical classification to evaluate the posterior probability of being a certain species to filter less confident decisions. This novel classification-rejection method cleans up decisions and rejects unknown classes. After constructing the tree architecture, a novel trajectory voting method is used to eliminate accumulated errors during hierarchical classification and, therefore, achieves better performance. The proposed BGOTR-based hierarchical classification method is applied to recognize the 15 major species of 24150 manually labelled fish images and to detect new species in an unrestricted natural environment recorded by underwater cameras in south Taiwan sea. It achieves significant improvements compared to the state-of-the-art techniques. Furthermore, the sequence of feature selection and constructing a multi-class SVM is investigated. We propose that an Individual Feature Selection (IFS) procedure can be directly exploited to the binary One-versus-One SVMs before assembling the full multiclass SVM. The IFS method selects different subsets of features for each Oneversus- One SVM inside the multiclass classifier so that each vote is optimized to discriminate the two specific classes. The proposed IFS method is tested on four different datasets comparing the performance and time cost. Experimental results demonstrate significant improvements compared to the normal Multiclass Feature Selection (MFS) method on all datasets.
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Harrysson, Mattias. "Neural probabilistic topic modeling of short and messy text." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189532.

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Exploring massive amount of user generated data with topics posits a new way to find useful information. The topics are assumed to be “hidden” and must be “uncovered” by statistical methods such as topic modeling. However, the user generated data is typically short and messy e.g. informal chat conversations, heavy use of slang words and “noise” which could be URL’s or other forms of pseudo-text. This type of data is difficult to process for most natural language processing methods, including topic modeling. This thesis attempts to find the approach that objectively give the better topics from short and messy text in a comparative study. The compared approaches are latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words, and a new approach based on previous work named Neural Probabilistic Topic Modeling (NPTM). It could only be concluded that NPTM have a tendency to achieve better topics on short and messy text than LDA and RO-LDA. GMM on the other hand could not produce any meaningful results at all. The results are less conclusive since NPTM suffers from long running times which prevented enough samples to be obtained for a statistical test.<br>Att utforska enorma mängder användargenererad data med ämnen postulerar ett nytt sätt att hitta användbar information. Ämnena antas vara “gömda” och måste “avtäckas” med statistiska metoder såsom ämnesmodellering. Dock är användargenererad data generellt sätt kort och stökig t.ex. informella chattkonversationer, mycket slangord och “brus” som kan vara URL:er eller andra former av pseudo-text. Denna typ av data är svår att bearbeta för de flesta algoritmer i naturligt språk, inklusive ämnesmodellering. Det här arbetet har försökt hitta den metod som objektivt ger dem bättre ämnena ur kort och stökig text i en jämförande studie. De metoder som jämfördes var latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words samt en egen metod med namnet Neural Probabilistic Topic Modeling (NPTM) baserat på tidigare arbeten. Den slutsats som kan dras är att NPTM har en tendens att ge bättre ämnen på kort och stökig text jämfört med LDA och RO-LDA. GMM lyckades inte ge några meningsfulla resultat alls. Resultaten är mindre bevisande eftersom NPTM har problem med långa körtider vilket innebär att tillräckligt många stickprov inte kunde erhållas för ett statistiskt test.
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Jiřík, Leoš. "Rozpoznávání pozic a gest." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235932.

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This thesis inquires the existing methods on the field of image recognition with regards to gesture recognition. Some methods have been chosen for deeper study and these are to be discussed later on. The second part goes in for the concenpt of an algorithm that would be able of robust gesture recognition based on data acquired within the AMI and M4 projects. A new ways to achieve precise information on participants position are suggested along with dynamic data processing approaches toward recognition. As an alternative, recognition using Gaussian Mixture Models and periodicity analysis are brought in. The gesture class in focus are speech supporting gestures. The last part demonstrates the results and discusses future work.
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Larsson, Alm Kevin. "Automatic Speech Quality Assessment in Unified Communication : A Case Study." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159794.

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Speech as a medium for communication has always been important in its ability to convey our ideas, personality and emotions. It is therefore not strange that Quality of Experience (QoE) becomes central to any business relying on voice communication. Using Unified Communication (UC) systems, users can communicate with each other in several ways using many different devices, making QoE an important aspect for such systems. For this thesis, automatic methods for assessing speech quality of the voice calls in Briteback’s UC application is studied, including a comparison of the researched methods. Three methods all using a Gaussian Mixture Model (GMM) as a regressor, paired with extraction of Human Factor Cepstral Coefficients (HFCC), Gammatone Frequency Cepstral Coefficients (GFCC) and Modified Mel Frequency Cepstrum Coefficients (MMFCC) features respectively is studied. The method based on HFCC feature extraction shows better performance in general compared to the two other methods, but all methods show comparatively low performance compared to literature. This most likely stems from implementation errors, showing the difference between theory and practice in the literature, together with the lack of reference implementations. Further work with practical aspects in mind, such as reference implementations or verification tools can make the field more popular and increase its use in the real world.
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Verdet, Florian. "Exploring variabilities through factor analysis in automatic acoustic language recognition." Phd thesis, Université d'Avignon, 2011. http://tel.archives-ouvertes.fr/tel-00954255.

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Language Recognition is the problem of discovering the language of a spoken definitionutterance. This thesis achieves this goal by using short term acoustic information within a GMM-UBM approach.The main problem of many pattern recognition applications is the variability of problemthe observed data. In the context of Language Recognition (LR), this troublesomevariability is due to the speaker characteristics, speech evolution, acquisition and transmission channels.In the context of Speaker Recognition, the variability problem is solved by solutionthe Joint Factor Analysis (JFA) technique. Here, we introduce this paradigm toLanguage Recognition. The success of JFA relies on several assumptions: The globalJFA assumption is that the observed information can be decomposed into a universalglobal part, a language-dependent part and the language-independent variabilitypart. The second, more technical assumption consists in the unwanted variability part to be thought to live in a low-dimensional, globally defined subspace. In this work, we analyze how JFA behaves in the context of a GMM-UBM LR framework. We also introduce and analyze its combination with Support Vector Machines(SVMs).The first JFA publications put all unwanted information (hence the variability) improvemen tinto one and the same component, which is thought to follow a Gaussian distribution.This handles diverse kinds of variability in a unique manner. But in practice,we observe that this hypothesis is not always verified. We have for example thecase, where the data can be divided into two clearly separate subsets, namely datafrom telephony and from broadcast sources. In this case, our detailed investigations show that there is some benefit of handling the two kinds of data with two separatesystems and then to elect the output score of the system, which corresponds to the source of the testing utterance.For selecting the score of one or the other system, we need a channel source related analyses detector. We propose here different novel designs for such automatic detectors.In this framework, we show that JFA's variability factors (of the subspace) can beused with success for detecting the source. This opens the interesting perspectiveof partitioning the data into automatically determined channel source categories,avoiding the need of source-labeled training data, which is not always available.The JFA approach results in up to 72% relative cost reduction, compared to the overall resultsGMM-UBM baseline system. Using source specific systems followed by a scoreselector, we achieve 81% relative improvement.
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Wu, Burton. "New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/46084/1/Burton_Wu_Thesis.pdf.

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This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.
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Book chapters on the topic "Gaussian mixer model (GMM)"

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Hussain, H., S. H. Salleh, C. M. Ting, A. K. Ariff, I. Kamarulafizam, and R. A. Suraya. "Speaker Verification Using Gaussian Mixture Model (GMM)." In IFMBE Proceedings. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21729-6_140.

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Li, Zhenyu, Fan Yu, Jie Lu, and Zhen Qian. "GMM-CoRegNet: A Multimodal Groupwise Registration Framework Based on Gaussian Mixture Model." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72069-7_59.

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Yu, Ziqiang, Mincai Lai, and Lin Wang. "GMM-KNN: A Method for Processing Continuous k-NN Queries Based on The Gaussian Mixture Model." In Hybrid Intelligent Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76351-4_4.

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Li, Chengxuan. "Visual Typology: A Numerical Taxonomy of Urban Spaces Using Isovist Analysis." In Computational Design and Robotic Fabrication. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3433-0_46.

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Abstract Urban spaces possess diverse visual qualities that significantly impact comfort, aesthetics, and navigation. This paper introduces a novel approach towards classifying urban spaces based on their visual characteristics through isovist analysis. An isovist is the polygon representing the visible areas from a given vantage point. The geometrical attributes of the isovist polygon enables a quantitative measure of visual qualities in the urban setting. However, the potential for classifying urban spaces based on the geometrical attributes of isovist polygons remains largely untapped. This paper presents a methodology to systematically categorise urban spaces using isovists and their geometrical attributes. By aggregating ten dimensions of geometrical attributes through a Gaussian Mixture Model (GMM) clustering analysis, this workflow produces a classifier that categorises urban spaces into 10 distinct spatial types, each possessing unique visual and spatial characteristics. This method successfully captures intrinsic spatial typologies across diverse urban contexts and can reflect the values embedded in urban design schemes. By facilitating meaningful and discussions in urban planning and design, this research contributes to a deeper and numerical understanding of the spatial and visual aspects of urban design. Further research avenues include the extension of this methodology to 3D analysis and refining tessellation algorithms for improved computational efficiency and accuracy.
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Xia, Changhai, Zhiping Peng, Delong Cui, Qirui Li, and Lihui Sun. "Concentration Diagnosis in Soft Sensing Based on Bayesian T-Distribution Mixture Regression." In Fuzzy Systems and Data Mining IX. IOS Press, 2023. http://dx.doi.org/10.3233/faia231042.

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This study presents a cutting-edge soft sensing approach for coke-making diagnostics, aimed at tackling the challenges posed by multifaceted, nonlinear, non-Gaussian, and noisy operational data prevalent in coke-making ovens. Our proposed method leverages a Bayesian t-distributed mixed regression model, effectively capturing the intricate nature of multivariate, nonlinear, and non-Gaussian data. The utilization of the t-distribution ensures the model’s resilience to interference, with model parameter estimation achieved within a Bayesian framework. Conducting simulation experiments and real industrial experiments, as well as comparative analysis with PLSR, GMR, and GPR models, we demonstrate the model’s good robustness, excellent prediction accuracy, and robustness, further confirming its potential application in coking diagnosis.
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Mayorga Pedro, Druzgalski Cristopher, GonzÁlez Oscar Hugo, Lopez Hernán Silverio, and Criollo Marco Antonio. "Assesment of Respiratory Diseases through Acoustic GMM Modeling." In Ambient Intelligence and Smart Environments. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-080-2-57.

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The purpose of this paper is to present a method that utilizes lung sounds (LS) for quantitative assessment of patient health, based on the fact that LS are relates to respiratory disorders. Traditional asthma evaluation methods may involve auscultation and spirometry. However, improved diagnosis opportunities can be offered by utilizing sensitive electronic stethoscopes (now widely available), and the application of quantitative signal analysis methods. In this context, we carried out experiments using normal LS from both the RALE repository and recordings from students. In this paper, we propose an acoustic evaluation methodology based on the Gaussian Mixed Models (GMM) that should assist in broader analysis, identification, and differentiation of LS. Additionally, frequency domain analysis of peculiar sounds reflected during wheezes and crackles, and their differentiation from normal respiratory sounds should assist in improved diagnosis.
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Gurjar, Priyanshu, Anurag Bhatnagar, and Nikhar Bhatnagar. "ASA: AUDIO SENTIMENT ANALYSIS AFTER A SINGLE-CHANNEL MULTIPLE SOURCE SEPARATION." In Futuristic Trends in Information Technology Volume 3 Book 2. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bfit2p8ch2.

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This paper aims at speaker diarization on an audio clip and sentiment analysis. Audio segmentation is done on the audio file using Voice Activity Detection (VAD). Once the audio is segmented, speaker identification is done using MAP estimation on every chunk using Universal Background Model (UBM) and Gaussian Mixture Model (GMM). Every chunk represents a different GMM while UBM represents a GMM on the whole audio file. Speech clustering is done using spectral clustering on every audio segment. Audio sentiment analysis is performed on a supervised emotion dataset (The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)) using Deep Neural Networks (DNN). The trained model was used to classify the sentiment of every chunk of the audio clip.
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Iyer, Kirtika, Abhay Shukla, Kunal Sharma, and Maya Varghese. "Speech Emotion Recognition using Gaussian Mixture Model (GMM) and K-Nearest Neighbors (KNN)." In Advancements in Communication and Systems. Soft Computing Research Society, 2024. http://dx.doi.org/10.56155/978-81-955020-7-3-39.

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This paper aimed to propose a novel methodology to improve the accuracy and efficiency of speech emotion recognition, through to the multilingual setting. The paper’s topic was the low precision obtained by the systems for speech emotion recognition, especially in multilingual settings. The research problem was the performance of the existing systems which could achieve merely 72% accuracy in recognizing the correct emotion from speech. The research’s importance was the enhancement of the performance of these systems in order to improve the user experience and the range of its applications in multilingual settings. The paper uses a research methodology with feature extraction methods and machine learning algorithms, such as Mel-frequency cepstral coefficients, zero-crossing rate, harmonic-to-noise ratio, such as Gaussian Mixture Models, and K-Nearest Neighbors. The proposed methodology analysis leads to a major increase in accuracy, attaining the performance of 82% in the complex multilingual environment. Besides, this research paper describes the areas for future research to allow additional improvement and overcome the possible weaknesses of the designed methodology, contributing to the development of the field.
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Lanka, Divya, and Selvaradjou Kandasamy. "An Unsupervised Traffic Modelling Framework in IoV Using Orchestration of Road Slicing." In Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4991-2.ch010.

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Presenting better traffic management in urban scenarios has endured as a challenge to reduce the fatality rate. The model developed consolidates an evolving methodology to handle traffic on roads in an abstract way in internet of vehicles (IoV) orchestrated with unsupervised machine learning (USL) techniques. At first, the roads are sliced into segments with roadside units (RSU) that provide vehicle to everything (V2X) communication in a multi-hop manner and examine traffic in real-time. The unique nature of the proposed framework is to introduce USL into the RSU to learn about traffic patterns. The RSU upon learning the traffic patterns applies the Gaussian mixture model (GMM) to observe the variation in the traffic pattern to immediately generate warning alerts and collision forward messages to the vehicles on road. The application of USL and GMM ensures speed control of vehicles with traffic alerts, thereby deteriorating the fatality rates.
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Khoulqi, Ichrak, Najlae Idrissi, and Muhammad Sarfraz. "Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization." In Research Anthology on Medical Informatics in Breast and Cervical Cancer. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7136-4.ch038.

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Breast cancer is one of the significant issues in medical sciences today. Specifically, women are suffering most worldwide. Early diagnosis can result to control the growth of the tumor. However, there is a need of high precision of diagnosis for right treatment. This chapter contributes toward an achievement of a computer-aided diagnosis (CAD) system. It deals with mammographic images and enhances their quality. Then, the enhanced images are segmented for pectoral muscle (PM) in the Medio-Lateral-Oblique (MLO) view of the mammographic images. The segmentation approach uses the tool of Gaussian Mixture Model-Expectation Maximization (GMM-EM). A standard database of Mini-MIAS with 322 images has been utilized for the implementation and experimentation of the proposed technique. The metrics of structural similarity measure and DICE coefficient have been utilized to verify the quality of segmentation based on the ground truth. The proposed technique is quite robust and accurate, it supersedes various existing techniques when compared in the same context.
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Conference papers on the topic "Gaussian mixer model (GMM)"

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Averroes, Fitra Luthfie, and Silmi Fauziati. "Clustering Analysis of PLN XYZ Customers Using K-Means and Gaussian Mixture Model (GMM) for Marketing Strategy." In 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2024. https://doi.org/10.1109/isriti64779.2024.10963373.

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Hiwatari, Takayuki, Fumiko Harada, and Hiromitsu Shimakawa. "Sensing Intra-clothing Climate to Increase Comfort According to time, place, and occasion." In 10th International Conference on Human Interaction and Emerging Technologies (IHIET 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004014.

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Humans wear comfortable clothing depending on time, place, and occasion (TPO) to improve their quality of life. The wearing comfort of clothes varies with the microclimate formed between the skin and the clothes, referred to as the intra-clothing climate. Though comfort is related to the intra-clothing climate, comfort can be sacrificed to some extent, depending on the TPO.This study refers to unbearable discomfort as UD. Clothing giving UD differs depending on the TPO. In addition, the temperature and humidity inside the clothes affect the probability people feel UD. The study aims to find the relationship of the probability of UD with the temperature and humidity inside the clothes for each TPO to recommend clothes that prevent UD from occurring according to the TPO.The study assumes that the probability density of comfort at a specific TPO follows a two-dimensional Gaussian distribution of the temperature and humidity inside the clothes. It means not the temperature and humidity inside the clothes around the mean of the Gaussian distribution but those at the foot of the distribution are assumed to cause UD. The paper proposes a method to verify the assumption through a temperature and humidity sensor in the clothing. A temperature/humidity sensor in a small mesh case is attached between the clothes and the skin with a strap around the neck, to obtain temperature/humidity data. In addition, the comfort at each TPO is obtained by questionnaire in the range of "0 (uncomfortable)" to "4 (comfortable)". The distribution of comfort for the climate inside clothes from the sensor data is compared with questionnaire results.Experiments with 3 male subjects in their 20s during the 2-week experiment period have been conducted to observe the climate inside clothes in real life. The observed data are examined to extract the relationship with comfort for each TPO. The experiment has shown that the degree of comfort changes like a mountain on the two-dimensional plane of the temperature and humidity inside the clothing for a specific TPO of the subject. It also demonstrates that the degree of unbearable comfort increases at its foot. The data with the highest comfort level for each TPO of each subject are extracted to be applied for the GMM, to obtain a mixed normal distribution representing the area with the highest comfort level. The result accounts for the degree of unbearable comfort increasing at the tail of each Gaussian model. These results can be used for clothing recommendation considering comfort for each TPO based on the temperature and humidity sensed inside the clothing.
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Gang Yu, Jun Sun, and Changning Li. "Machine performance assessment using Gaussian mixture model (GMM)." In 2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics (ISSCAA). IEEE, 2008. http://dx.doi.org/10.1109/isscaa.2008.4776183.

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Ozbek, I. Yucel, and Mubeccel Demirekler. "Audiovisual articulatory inversion based on Gaussian Mixture Model (GMM)." In 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU). IEEE, 2010. http://dx.doi.org/10.1109/siu.2010.5653987.

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Anand, Vishnu, Durgakant Pushp, Rishin Raj, and Kaushik Das. "Gaussian Mixture Model (GMM) Based Dynamic Object Detection and Tracking." In 2019 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2019. http://dx.doi.org/10.1109/icuas.2019.8797927.

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Sri, Tirumalasetti Teja, Suraki Ravi Kishan, Kakani Naga Rahul Chowdary, and Pandiri Sainath. "Build a Model for Speech Emotion Recognition using Gaussian Mixture Model (GMM)." In 2023 2nd International Conference on Futuristic Technologies (INCOFT). IEEE, 2023. http://dx.doi.org/10.1109/incoft60753.2023.10425275.

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Li, Guanglin, Bin Li, Changsheng Chen, Shunquan Tan, and Guoping Qiu. "Learning General Gaussian Mixture Model with Integral Cosine Similarity." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/444.

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Gaussian mixture model (GMM) is a powerful statistical tool in data modeling, especially for unsupervised learning tasks. Traditional learning methods for GMM such as expectation maximization (EM) require the covariance of the Gaussian components to be non-singular, a condition that is often not satisfied in real-world applications. This paper presents a new learning method called G$^2$M$^2$ (General Gaussian Mixture Model) by fitting an unnormalized Gaussian mixture function (UGMF) to a data distribution. At the core of G$^2$M$^2$ is the introduction of an integral cosine similarity (ICS) function for comparing the UGMF and the unknown data density distribution without having to explicitly estimate it. By maximizing the ICS through Monte Carlo sampling, the UGMF can be made to overlap with the unknown data density distribution such that the two only differ by a constant scalar, and the UGMF can be normalized to obtain the data density distribution. A Siamese convolutional neural network is also designed for optimizing the ICS function. Experimental results show that our method is more competitive in modeling data having correlations that may lead to singular covariance matrices in GMM, and it outperforms state-of-the-art methods in unsupervised anomaly detection.
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"Gaussian Mixture Model Based Damage Evaluation for Aircraft Structures." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-18.

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Abstract. The Guided Wave (GW) based Structural Health Monitoring (SHM) method is of significant research interest because of its wide monitoring range and high sensitivity. However, there are still many challenges in real engineering applications due to complex time-varying conditions, such as changes in temperature and humidity, random dynamic loads, and structural boundary conditions. In this paper, a Gaussian Mixture Model (GMM) is adopted to deal with these problems. Multi-dimensional GMM (MDGMM) is proposed to model the probability distribution of GW features under time-varying conditions. Furthermore, to measure the migration degree of MDGMM to reveal the crack propagation, research on migration indexes of the probability model is carried out. Finally, the validation in an aircraft fatigue test shows a good performance of the MDGMM.
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Hiwase, Shrikant Deokrishna, PRAMOD JAGTAP, and Dinesh Krishna. "Engine Overheating Prediction with Machine Learning Using Gaussian Mixture Model (GMM)." In 10TH SAE India International Mobility Conference. SAE International, 2022. http://dx.doi.org/10.4271/2022-28-0007.

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Hu, Zhen, and Sankaran Mahadevan. "Bayesian Network Learning for Uncertainty Quantification." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68187.

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Bayesian Networks (BNs) are being studied in recent years for system diagnosis, reliability analysis, and design of complex engineered systems. In several practical applications, BNs need to be learned from available data before being used for design or other purposes. Current BN learning algorithms are mainly developed for networks with only discrete variables. Engineering design problems often consist of both discrete and continuous variables. This paper develops a framework to handle continuous variables in BN learning by integrating learning algorithms of discrete BNs with Gaussian mixture models (GMMs). We first make the topology learning more robust by optimizing the number of Gaussian components in the univariate GMMs currently available in the literature. Based on the BN topology learning, a new Multivariate Gaussian Mixture (MGM) strategy is developed to improve the accuracy of conditional probability learning in the BN. A method is proposed to address this difficulty of MGM modeling with data of mixed discrete and continuous variables by mapping the data for discrete variables into data for a standard normal variable. The proposed framework is capable of learning BNs without discretizing the continuous variables or making assumptions about their CPDs. The applications of the learned BN to uncertainty quantification and model calibration are also investigated. The results of a mathematical example and an engineering application example demonstrate the effectiveness of the proposed framework.
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