• Title, Summary, Keyword: Gaussian mixture model

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L1-norm Regularization for State Vector Adaptation of Subspace Gaussian Mixture Model (L1-norm regularization을 통한 SGMM의 state vector 적응)

  • Goo, Jahyun;Kim, Younggwan;Kim, Hoirin
    • Phonetics and Speech Sciences
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    • v.7 no.3
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    • pp.131-138
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    • 2015
  • In this paper, we propose L1-norm regularization for state vector adaptation of subspace Gaussian mixture model (SGMM). When you design a speaker adaptation system with GMM-HMM acoustic model, MAP is the most typical technique to be considered. However, in MAP adaptation procedure, large number of parameters should be updated simultaneously. We can adopt sparse adaptation such as L1-norm regularization or sparse MAP to cope with that, but the performance of sparse adaptation is not good as MAP adaptation. However, SGMM does not suffer a lot from sparse adaptation as GMM-HMM because each Gaussian mean vector in SGMM is defined as a weighted sum of basis vectors, which is much robust to the fluctuation of parameters. Since there are only a few adaptation techniques appropriate for SGMM, our proposed method could be powerful especially when the number of adaptation data is limited. Experimental results show that error reduction rate of the proposed method is better than the result of MAP adaptation of SGMM, even with small adaptation data.

A Density-based Clustering Method

  • Ahn, Sung Mahn;Baik, Sung Wook
    • Communications for Statistical Applications and Methods
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    • v.9 no.3
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    • pp.715-723
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    • 2002
  • This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values

A Hardware Implementation of Moving Object Detection Algorithm using Gaussian Mixture Model (가우시안 혼합 모델을 이용한 이동 객체 검출 알고리듬의 하드웨어 구현)

  • Kim, Gyeong-hun;An, Hyo-Sik;Shin, Kyung-wook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.407-409
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    • 2015
  • In this paper, a hardware implementation of MOD(Moving Object Detection) algorithm is described, which is based GMM(Gaussian Mixture Model) and background subtraction. The EGML(Effective Gaussian Mixture Learning) is used to model and update background. Some approximations of EGML calculations are applied to reduce hardware complexity, and pipelining technique is used to improve operating speed. Gaussian parameters are adjustable according to various environment conditions to achieve better MOD performance. MOD processor is verified by using FPGA-in-the-loop verification, and it can operate with 109 MHz clock frequency on XC5VSX95T FPGA device.

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Gaussian Mixture based K2 Rifle Chamber Pressure Modeling of M193 and K100 Bullets (가우시안 혼합모델 기반 탄종별 K2 소화기의 약실압력 모델링)

  • Kim, Jong-Hwan;Lee, Byounghwak;Kim, Kyoungmin;Shin, Kyuyong;Lee, Wonwoo
    • Journal of the Korea Institute of Military Science and Technology
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    • v.22 no.1
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    • pp.27-34
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    • 2019
  • This paper presents a chamber pressure model development of K2 rifle by applying Gaussian mixture model. In order to materialize a real recoil force of a virtual reality shooting rifle in military combat training, the chamber pressure which is one of major components of the recoil force needs to be investigated and modeled. Over 200,000 data of the chamber pressure were collected by implementing live fire experiments with both K100 and M193 of 5.56 mm bullets. Gaussian mixture method was also applied to create a mathematical model that satisfies nonlinear, asymmetry, and deviations of the chamber pressure which is caused by irregular characteristics of propellant combustion. In addition, Polynomial and Fourier Regression were used for comparison of results, and the sum of squared errors, the coefficient of determination and root-mean-square errors were analyzed for performance measurement.

IMAGE DENOISING BASED ON MIXTURE DISTRIBUTIONS IN WAVELET DOMAIN

  • Bae, Byoung-Suk;Lee, Jong-In;Kang, Moon-Gi
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.246-249
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    • 2009
  • Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

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A Gaussian Mixture Model Based Pattern Classification Algorithm of Forearm Electromyogram (Gaussian Mixture Model 기반 전완 근전도 패턴 분류 알고리즘)

  • Song, Y.R.;Kim, S.J.;Jeong, E.C.;Lee, S.M.
    • Journal of rehabilitation welfare engineering & assistive technology
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    • v.5 no.1
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    • pp.95-101
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    • 2011
  • In this paper, we propose the gaussian mixture model based pattern classification algorithm of forearm electromyogram. We define the motion of 1-degree of freedom as holding and unfolding hand considering a daily life for patient with prosthetic hand. For the extraction of precise features from the EMG signals, we use the difference absolute mean value(DAMV) and the mean absolute value(MAV) to consider amplitude characteristic of EMG signals. We also propose the D_DAMV and D_MAV in order to classify the amplitude characteristic of EMG signals more precisely. In this paper, we implemented a test targeting four adult male and identified the accuracy of EMG pattern classification of two motions which are holding and unfolding hand.

A New Distance Measure for a Variable-Sized Acoustic Model Based on MDL Technique

  • Cho, Hoon-Young;Kim, Sang-Hun
    • ETRI Journal
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    • v.32 no.5
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    • pp.795-800
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    • 2010
  • Embedding a large vocabulary speech recognition system in mobile devices requires a reduced acoustic model obtained by eliminating redundant model parameters. In conventional optimization methods based on the minimum description length (MDL) criterion, a binary Gaussian tree is built at each state of a hidden Markov model by iteratively finding and merging similar mixture components. An optimal subset of the tree nodes is then selected to generate a downsized acoustic model. To obtain a better binary Gaussian tree by improving the process of finding the most similar Gaussian components, this paper proposes a new distance measure that exploits the difference in likelihood values for cases before and after two components are combined. The mixture weight of Gaussian components is also introduced in the component merging step. Experimental results show that the proposed method outperforms MDL-based optimization using either a Kullback-Leibler (KL) divergence or weighted KL divergence measure. The proposed method could also reduce the acoustic model size by 50% with less than a 1.5% increase in error rate compared to a baseline system.

A study on Gaussian mixture model deep neural network hybrid-based feature compensation for robust speech recognition in noisy environments (잡음 환경에 효과적인 음성 인식을 위한 Gaussian mixture model deep neural network 하이브리드 기반의 특징 보상)

  • Yoon, Ki-mu;Kim, Wooil
    • The Journal of the Acoustical Society of Korea
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    • v.37 no.6
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    • pp.506-511
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    • 2018
  • This paper proposes an GMM(Gaussian Mixture Model)-DNN(Deep Neural Network) hybrid-based feature compensation method for effective speech recognition in noisy environments. In the proposed algorithm, the posterior probability for the conventional GMM-based feature compensation method is calculated using DNN. The experimental results using the Aurora 2.0 framework and database demonstrate that the proposed GMM-DNN hybrid-based feature compensation method shows more effective in Known and Unknown noisy environments compared to the GMM-based method. In particular, the experiments of the Unknown environments show 9.13 % of relative improvement in the average of WER (Word Error Rate) and considerable improvements in lower SNR (Signal to Noise Ratio) conditions such as 0 and 5 dB SNR.

Frequency Domain Double-Talk Detector Based on Gaussian Mixture Model (주파수 영역에서의 Gaussian Mixture Model 기반의 동시통화 검출 연구)

  • Lee, Kyu-Ho;Chang, Joon-Hyuk
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.4
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    • pp.401-407
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    • 2009
  • In this paper, we propose a novel method for the cross-correlation based double-talk detection (DTD), which employing the Gaussian Mixture Model (GMM) in the frequency domain. The proposed algorithm transforms the cross correlation coefficient used in the time domain into 16 channels in the frequency domain using the discrete fourier transform (DFT). The channels are then selected into seven feature vectors for GMM and we identify three different regions such as far-end, double-talk and near-end speech using the likelihood comparison based on those feature vectors. The presented DTD algorithm detects efficiently the double-talk regions without Voice Activity Detector which has been used in conventional cross correlation based double-talk detection. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional schemes. especially, show the robustness against detection errors resulting from the background noises or echo path change which one of the key issues in practical DTD.