• Journal of Information Display
• /
• v.2 no.3
• /
• pp.18-31
• /
• 2001

We utilize Fourier transform infrared (IR) spectroscopy to probe intramolecular hydrogen bonding in $smectic-C^{\ast}$ liquid crystal phases. Infrared spectra of aligned smectic liquid crystal materials vs. temperature and of isotropic liquid crystal mixtures vs. concentration were measured in homologs, both with and without hydrogen bonding. Hydrogen bonding significantly changes the direction and magnitude of the vibrational dipole transition moments, causing marked changes in the IR dichroic absorbance profiles of hydrogen bonded molecular subfragments. A GAUSSIAN94 computation of the directions, magnitudes, and frequencies of the vibrational dipole moments of molecular subfragments shows good agreement with the experimental data. The results show that IR dichroism can be an effective probe of hydrogen bonding in liquid crystal phases.

• MALSORI
• /
• no.65
• /
• pp.67-80
• /
• 2008

We have been building a text-independent speaker recognition system that is robust to unknown channel and noise environments. In this paper, we propose a linear transformation to obtain robust features. The transformation is optimized to maximize the distances between the Gaussian mixtures. We use rotation of the axes, to cope with the problem of scaling the transformation matrix. The proposed transformation is similar to PCA or LDA, but can achieve better result in some special cases where PCA and LDA can not work properly. We use YOHO database to evaluate the proposed method and compare the result with PCA and LDA. The results show that the proposed method outperforms all the baseline, PCA and LDA.

• Speech Sciences
• /
• v.10 no.4
• /
• pp.149-157
• /
• 2003

In this paper, we proposed the principal component analysis (PCA) fuzzy mixture model for speaker identification. A PCA fuzzy mixture model is derived from the combination of the PCA and the fuzzy version of mixture model with diagonal covariance matrices. In this method, the feature vectors are first transformed by each speaker's PCA transformation matrix to reduce the correlation among the elements. Then, the fuzzy mixture model for speaker is obtained from these transformed feature vectors with reduced dimensions. The orthogonal Gaussian Mixture Model (GMM) can be derived as a special case of PCA fuzzy mixture model. In our experiments, with having the number of mixtures equal, the proposed method requires less training time and less storage as well as shows better speaker identification rate compared to the conventional GMM. Also, the proposed one shows equal or better identification performance than the orthogonal GMM does.

• Journal of KIEE
• /
• v.11 no.1
• /
• pp.1-7
• /
• 2001

Various methods for blind source separation (BSS) are based on independent component analysis (ICA) which can be viewed as a nonlinear extension of principal component analysis (PCA). Most existing ICA methods require certain nonlinear functions (which leads to higher-order statistics) depending on the probability distributions of sources, whereas PCA is a linear learning method based on second-order statistics. In this paper we show that the PCA can be applied to the task of BBS, provided that source are spatially uncorrelated but temporally correlated. Since the resulting method is based on only second-order statistics, it avoids the nonlinear function and is able to separate mixtures of several colored Gaussian sources, in contrast to the conventional ICA methods.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.5
• /
• pp.112-121
• /
• 1997

In this paper, we study a model parameter compensation method for noise-robust speech recognition. We study model parameter compensation on a sentence by sentence and no other informations are used. Parallel model combination(PMC), well known as a model parameter compensation algorithm, is implemented and used for a reference of performance comparision. We also propose a modified PMC method which tunes model parameter with an association factor that controls average variability of gaussian mixtures and variability of single gaussian mixture per state for more robust modeling. We obtain a re-estimation solution of environmental variables based on the expectation-maximization(EM) algorithm in the cepstral domain. To evaluate the performance of the model compensation methods, we perform experiments on speaker-independent isolated word recognition. Noise sources used are white gaussian and driving car noise. To get corrupted speech we added noise to clean speech at various signal-to-noise ratio(SNR). We use noise mean and variance modeled by 3 frame noise data. Experimental result of the VTS approach is superior to other methods. The scheme of the zero order VTS approach is similar to the modified PMC method in adapting mean vector only. But, the recognition rate of the Zero order VTS approach is higher than PMC and modified PMC method based on log-normal approximation.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.2
• /
• pp.817-821
• /
• 2012

In this paper we applied the optimal mixture number estimation technique in GMM(Gaussian mixture model) using BIC(Bayesian information criterion) and MDL(minimum description length) as a model selection criterion for environmental sounds recognition. In the experiment, we extracted 12 MFCC(mel-frequency cepstral coefficients) features from 9 kinds of environmental sounds which amounts to 27747 data and classified them with GMM. As mentioned above, BIC and MDL is applied to estimate the optimal number of mixtures in each environmental sounds class. According to the experimental results, while the recognition performances are maintained, the computational complexity decreases by 17.8% with BIC and 31.7% with MDL. It shows that the computational complexity reduction by BIC and MDL is effective for environmental sounds recognition using GMM.

• The Journal of the Korea Contents Association
• /
• v.11 no.12
• /
• pp.497-503
• /
• 2011

In this paper, we proposed the MCE as a GMM training method to improve the performance of environmental sounds recognition. We model the environmental sounds data with newly defined misclassification function using the log likelihood of the corresponding class and the log likelihood of the rest classes for discriminative training. The model parameters are estimated with the loss function using GPD(generalized probabilistic descent). For recognition performance comparison, we extracted the 12 degrees features using preprocessing and MFCC(mel-frequency cepstral coefficients) of the 9 kinds of environmental sounds and carry out GMM classification experiments. According to the experimental results, MCE training method showed the best performance by an average of 87.06% with 19 mixtures. This result confirmed us that MCE training method could be effectively used as a GMM training method in environmental sounds recognition.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.8
• /
• pp.711-716
• /
• 2003

This paper proposes the method to select the number of optimal mixtures in each state in Continuous Density HMM (Hidden Markov Models), Previously, researchers used the same number of mixture components in each state of HMM regardless spectral characteristic of speaker, To model each speaker as accurately as possible, we propose to use a different number of mixture components for each state, Selection of mixture components considered the probability value of mixture by each state that affects much parameter estimation of continuous density HMM, Also, we use PCA (principal component analysis) to reduce the correlation and obtain the system' stability when it is reduced the number of mixture components, We experiment it when the proposed method used average 10% small mixture components than the conventional HMM, When experiment result is only applied selection of mixture components, the proposed method could get the similar performance, When we used principal component analysis, the feature vector of the 16 order could get the performance decrease of average 0,35% and the 25 order performance improvement of average 0.65%.

• Journal of KIISE:Databases
• /
• v.28 no.3
• /
• pp.324-333
• /
• 2001

In this paper we present a novel color image segmentation algorithm based on a Gaussian Mixture Model(GMM). It is introduced a Deterministic Annealing Expectation Maximization(DAEM) algorithm which is developed using the principle of maximum entropy to overcome the local maxima problem associated with the standard EM algorithm. In our approach, the GMM is used to represent the multi-colored objects statistically and its parameters are estimated by DAEM algorithm. We also develop the automatic determination method of the number of components in Gaussian mixtures models. The segmentation of image is based on the maximum posterior probability distribution which is calculated by using the GMM. The experimental results show that the proposed DAEM can estimate the parameters more accurately than the standard EM and the determination method of the number of mixture models is very efficient. When tested on two natural images, the proposed algorithm performs much better than the traditional algorithm in segmenting the image fields.

• Journal of Photoscience
• /
• v.12 no.2
• /
• pp.87-93
• /
• 2005

A new assay technique to distinguish between pure compounds and the isomeric mixtures has been suggested using single molecule (SM) fluorescence detection technique. Since the number of emission spots in a fluorophorespread film prepared from a genuine dye solution was determined by experimental condition, the deviation of spot numbers from the expected values could be considered to be an indication of lower purity of the sample solution. The lower limit of sample concentration for this assay was determined to be $5{\times}10^{-10}$ M to show uniform number of expected spots within 10% uncertainties in our experimental condition. An individual fluorescence intensity distribution for a mixture of isomers having doubly different emissivities was simulated by adding distributions obtained from Cy3 and nile red (NR) independently. The result indicated that the mixture could be identified from the pure compounds through the difference in the number of Gaussian functions to fit the distribution. This new assay technique can be applied to the purity test for synthetic biofluorophores which are usually prepared in small quantities not enough for classical ensemble assays.