• Economic and Environmental Geology
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• v.50 no.1
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• pp.15-26
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• 2017

Advanced argillic, argillic, and phyllic zones are the most important alteration patterns to predict the hidden ore body during exploration of hydrothermal deposits. We examined the quantitative relationship between the spectral absorption characteristics and the mineral content of the synthetic mixtures such as alunite-kaolinite and illite-kaolinite using short wavelength infrared (SWIR) spectroscopy. In the alunite-kaolinite mixtures, the spectral absorption characteristics of the alunite was highly correlated with the Hull quotient reflectance(0.99) and the kaolinite had the highest correlation with the Gaussian peak(0.92). Illite-kaolinite mixtures are essential for Gaussian deconvolution because of the overlap of absorption region. Illite and kaolinite mixtures indicate the high correlation of 0.93 and 0.98, respectively. The error ranges in the alunite-kaolinite(8%) and illite-kaolinite mixtures(5%) derived from SWIR were smaller than the ones(29% and 26%) obtained from X-ray diffraction(Rietveld) analysis. These results show that SWIR spectroscopic analysis is more reliable than XRD Rietveld analysis in terms of quantification of allowed minerals.

• Speech Sciences
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• v.14 no.1
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• pp.151-162
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• 2007

This study focuses on the classification of pathological voice using GMM (Gaussian Mixture Model) and compares the results to the previous work which was done by ANN (Artificial Neural Network). Speech data from normal people and patients were collected, then diagnosed and classified into two different categories. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chosen. Then the classification method based on the artificial neural network and Gaussian mixture method was employed to discriminate the data into normal and pathological speech. The GMM method attained 98.4% average correct classification rate with training data and 95.2% average correct classification rate with test data. The different mixture number (3 to 15) of GMM was used in order to obtain an optimal condition for classification. We also compared the average classification rate based on GMM, ANN and HMM. The proper number of mixtures on Gaussian model needs to be investigated in our future work.

• Speech Sciences
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• v.8 no.4
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• pp.17-33
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• 2001

In this paper, we implement the large variable vocabulary speech recognition system, which is characterized by no additional pre-training process and no limitation of recognized word list. We have designed the system in order to achieve the high recognition rate using the decision tree based state tying algorithm and in order to reduce the processing time using the gaussian selection based variable flooring algorithm, the limitation algorithm of the number of nodes and ENNS algorithm. The gaussian selection based variable flooring algorithm shows that it can reduce the total processing time by more than half of the recognition time, but it brings about the reduction of recognition rate. In other words, there is a trade off between the recognition rate and the processing time. The limitation algorithm of the number of nodes shows the best performance when the number of gaussian mixtures is a three. Both of the off-line and on-line experiments show the same performance. In our experiments, there are some differences of the recognition rate and the average recognition time according to the distinction of genders, speakers, and the number of vocabulary.

• The Journal of the Acoustical Society of Korea
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• v.24 no.3
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• pp.141-149
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• 2005

Tn general. Gaussian mixture model (GMM) is used to estimate the speaker model from the speech for speaker identification. The parameter estimates of the GMM are obtained by using the Expectation-Maximization (EM) algorithm for the maximum likelihood (ML) estimation. However the EM algorithm has such drawbacks that it depends heavily on the initialization and it needs the number of mixtures to be known. In this paper, to solve the above problems of the EM algorithm. we propose an EM algorithm with the initialization based on incremental ${\cal}k-means$ for GMM. The proposed method dynamically increases the number of mixtures one by one until finding the optimum number of mixtures. Whenever adding one mixture, we calculate the mutual relationship between it and one of other mixtures respectively. Finally. based on these mutual relationships. we can estimate the optimal number of mixtures which are statistically independent. The effectiveness of the proposed method is shown by the experiment for artificial data. Also. we performed the speaker identification by applying the proposed method comparing with other approaches.

• Proceedings of the KSPS conference
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• 2005.04a
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• pp.87-90
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• 2005

Today's state-of the-art speech recognition systems typically use continuous distribution hidden Markov models with the mixtures of Gaussian distributions. To obtain higher recognition accuracy, the hidden Markov models typically require huge number of Gaussian distributions. Such speech recognition systems have problems that they require too much memory to run, and are too slow for large applications. Many approaches are proposed for the design of compact acoustic models. One of those models is subspace distribution clustering hidden Markov model. Subspace distribution clustering hidden Markov model can represent original full-space distributions as some combinations of a small number of subspace distribution codebooks. Therefore, how to make the codebook is an important issue in this approach. In this paper, we report some experimental results on various quantization methods to make more accurate models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.1870-1877
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• 2012

Transient signals generally have short duration and variable length with time-varying and non-stationary characteristics. Thus frame-based pattern matching method is useful for classification of transient signals. In this paper, we propose a new method for classification of underwater transient signals using a Gaussian mixture model(GMM). We carried out classification experiments for various underwater transient signals depending upon the types of noise, signal-to-noise ratio, and number of mixtures in the GMM. Experimental results have verified that the proposed method works quite well for classification of underwater transient signals.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.175-186
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• 2021

Due to the increasing environmental pollution caused by scrap tires, a solution is being sought to recycle and use them in a field of civil engineering, i.e., construction. This paper will provide a brief overview of previous researches that give detailed information on the advantages and disadvantages, considering the microstructural and mechanical characteristics of self-compacting concrete, when waste tire rubber as an aggregate is added. With this aim, a database of 144 different mixtures of self-compacting concrete with partial substitute of natural aggregate with recycled tire rubber (self-compacting rubberized concrete, SCRC) provided by various researchers was created. In this study we show that Gaussian process regression (GPR) modelling is an appropriate method for predicting compressive strength of SCC with recycled tire rubber particles and is in accordance with the results displayed by SEM images.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2004-2007
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• 2003

This paper describes a method of performance enhancement using Flatness Mesure(FM) for the Gaussian Mixture Model(GMM) face recognition systems. Using this measure we discard the frames having low information before training and test. As the result, the performance increases about 9％ in the lower mixtures and calculation burden is decreased. As well, the recognition error rate is decreased under the illumination change surroundings. We use the 2D DCT coefficients lot face feature vectors and experiments are carried out on the Olivetti Research Laboratory (ORL) face database.

• The Journal of the Acoustical Society of Korea
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• v.30 no.6
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• pp.308-314
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• 2011

This paper presents techniques for estimating directions of multiple sound sources ranging from $0^{\circ}$ to $360^{\circ}$ using circular probability distributions having a periodic property. Phase differences containing direction information of sources can be modeled as mixtures of multiple probability distributions and source directions can be estimated by maximizing log-likelihood functions. Although the von Mises distribution is widely used for analyzing this kind of periodic data, we define a new class of circular probability distributions from Gaussian and Laplacian distributions by adopting a modulo operation to have $2{\pi}$-periodicity. Direction estimation with these circular probability distributions is done by implementing corresponding EM (Expectation-Maximization) algorithms. Simulation results in various reverberant environments confirm that Laplacian distribution provides better performance than von Mises and Gaussian distributions.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.459-463
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• 2009

Background modeling and motion detection is the one of the most significant real time video processing technique. Until now, many researches are conducted into the topic but it still needs much time for robustness. It is more important when other algorithms are used together such as object tracking, classification or behavior understanding. In this paper, we propose efficient multi-modal background modeling methods which can be understood as simplified learning method of Gaussian mixture model. We present its validity using numerical methods and experimentally show detecting performance.