• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.123-128
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• 2011

In this paper, we propose the improved noise power estimation in speech enhancement under various noise environments. The previous MS algorithm tracking the minimum value of finite search window uses the optimal power spectrum of signal for smoothing and adopts minimum probability. From the investigation of the previous MS-based methods it can be seen that a fixed size of the minimum search window is assumed regardless of the various environment. To achieve the different search window size, we use the noise classification algorithm based on the Gaussian mixture model (GMM). Performance of the proposed enhancement algorithm is evaluated by ITU-T P.862 perceptual evaluation of speech quality (PESQ) under various noise environments. Based on this, we show that the proposed algorithm yields better result compared to the conventional MS method.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.718-722
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• 2015

This paper proposes a fractional-order PID (Proportional-Integral-Derivative) control used electromagnetic strip stabilization controller in a continuous galvanizing line. Compared to a conventional PID controller, a fractional-order PID controller has integration-fractional-order and derivation-fractional-order as additional control parameters. Thanks to increased control parameters, more precise controller adjustment is available. In addition, accurate transfer function of a real system generally has a fractional-order form. Therefore, it is more adequate to use a fractional-order PID controller than a conventional PID controller for a real world system. Finite element models of a $1200{\times}2000{\times}0.8mm$ strip, which were extracted using a commercial software ANSYS were used as simulation plants, and Gaussian mixture models were used to find optimized control parameters that can reduce the strip vibrations to the lowest amplitude. Simulation results show that a fractional-order PID controller significantly reduces strip vibration and transient response time than a conventional PID controller.

• Journal of Biomedical Engineering Research
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• v.33 no.2
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• pp.65-71
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• 2012

In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.5
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• pp.104-111
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• 2002

This paper presents a fuzzy rule extraction method using EM(Expectation-Maximization) algorithm and a design method of adaptive neuro-fuzzy control. EM algorithm is used to estimate a maximum likelihood of a GMM(Gaussian Mixture Model) and cluster centers. The estimated clusters is used to automatically construct the fuzzy rules and membership functions for ANFIS(Adaptive Neuro-Fuzzy Inference System). Finally, we applied the proposed method to the water temperature control system and obtained better results with respect to the number of rules and SAE(Sum of Absolute Error) than previous techniques such as conventional fuzzy controller.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.138-142
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• 2009

We propose an effective voice-phishing detection algorithm based on discriminative weight training. The detection of voice phishing is performed based on a Gaussian mixture model (GMM) incorporaiting minimum classification error (MCE) technique. Actually, the MCE technique is based on log-likelihood from the decoding parameter of the SMV(Selectable Mode Vocoder) directly extracted from the decoding process in the mobile phone. According to the experimental result, the proposed approach is found to be effective for the voice phishing detection.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.195-206
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• 2016

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.76-81
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• 2009

We propose an effective emotion recognition algorithm based on the minimum classification error (MCE) incorporating multi-modal system The emotion recognition is performed based on a Gaussian mixture model (GMM) based on MCE method employing on log-likelihood. In particular, the reposed technique is based on the fusion of feature vectors based on voice signal and galvanic skin response (GSR) from the body sensor. The experimental results indicate that performance of the proposal approach based on MCE incorporating the multi-modal system outperforms the conventional approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.387-391
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• 2008

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.216-221
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• 2010

In this paper, we propose speaker verification method using Support Vector Machine (SVM) kernel with Gaussian Mixture Model (GMM)-supervector based on the Mahalanobis distance. The proposed GMM-supervector SVM kernel method is combined GMM with SVM. The GMM-supervectors are generated by GMM parameters of speaker and other speaker utterances. A speaker verification threshold of GMM-supervectors is decided by SVM kernel based on Mahalanobis distance to improve speaker verification accuracy. The experimental results for text-independent speaker verification using 20 speakers demonstrates the performance of the proposed method compared to GMM, SVM, GMM-supervector SVM kernel based on Kullback-Leibler (KL) divergence, and GMM-supervector SVM kernel based on Bhattacharyya distance.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.967-974
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• 2012

In this paper, a vehicle detection algorithm with HD CCTV camera images using GMM(Gaussian Mixture Model) algorithm and mathematical morphological processing is proposed. At the first stage, background could be estimated using GMM from CCTV input image signal and then object could be separated from difference image of the input image and background image. At the second stage, candidated object were reformed by using mathematical morphological processing. Finally, vehicle object could be detected using vehicle size informations depend on distance and vehicle type in tunnel. Through real experiments in tunnel, it is shown that the proposed system works well.