• Title/Summary/Keyword: Maximum a Posteriori Probability

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Speech Enhancement based on Minima Controlled Recursive Averaging Technique Incorporating Second-order Conditional Maximum a posteriori Criterion (2차 조건 사후 최대 확률 기반 최소값 제어 재귀평균기법을 이용한 음성향상)

  • Kum, Jong-Mo;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.4
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    • pp.132-138
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    • 2009
  • In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) which is based on the second-order conditional maximum a posteriori (CMAP). From an investigation of the MCRA scheme, it is discovered that the MCRA method cannot take full consideration of the inter-frame correlation of voice activity since the noise power estimate is adjusted by the speech presence probability depending on an observation of the current frame. To avoid this phenomenon, the proposed MCRA approach incorporates the second-order CMAP criterion in which the noise power estimate is obtained using the speech presence probability conditioned on both the current observation and the speech activity decisions in the previous two frames. Experimental results show that the proposed MCRA technique based on second-order conditional MAP yields better results compared to the conventional MCRA method.

A Minimum-Error-Rate Training Algorithm for Pattern Classifiers and Its Application to the Predictive Neural Network Models (패턴분류기를 위한 최소오차율 학습알고리즘과 예측신경회로망모델에의 적용)

  • 나경민;임재열;안수길
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.12
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    • pp.108-115
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    • 1994
  • Most pattern classifiers have been designed based on the ML (Maximum Likelihood) training algorithm which is simple and relatively powerful. The ML training is an efficient algorithm to individually estimate the model parameters of each class under the assumption that all class models in a classifier are statistically independent. That assumption, however, is not valid in many real situations, which degrades the performance of the classifier. In this paper, we propose a minimum-error-rate training algorithm based on the MAP (Maximum a Posteriori) approach. The algorithm regards the normalized outputs of the classifier as estimates of the a posteriori probability, and tries to maximize those estimates. According to Bayes decision theory, the proposed algorithm satisfies the condition of minimum-error-rate classificatin. We apply this algorithm to NPM (Neural Prediction Model) for speech recognition, and derive new disrminative training algorithms. Experimental results on ten Korean digits recognition have shown the reduction of 37.5% of the number of recognition errors.

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Design of Low-Density Parity-Check Codes for Multiple-Input Multiple-Output Systems (Multiple-Input Multiple-output system을 위한 Low-Density Parity-Check codes 설계)

  • Shin, Jeong-Hwan;Chae, Hyun-Do;Han, In-Duk;Heo, Jun
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.7C
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    • pp.587-593
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    • 2010
  • In this paper we design an irregular low-density parity-check (LDPC) code for multiple-input multiple-output (MIMO) system, using a simple extrinsic information transfer (EXIT) chart method. The MIMO systems considered are optimal maximum a posteriori probability (MAP) detector. The MIMO detector and the LDPC decoder exchange soft information and form a turbo iterative receiver. The EXIT charts are used to obtain the edge degree distribution of the irregular LDPC code which is optimized for the MIMO detector. It is shown that the performance of the designed LDPC code is better than that of conventional LDPC code which was optimized for either the Additive White Gaussian Noise (AWGN) channel or the MIMO channel.

A method of background noise removal of Raman spectra for classification of liver disease (간 질병 분류를 위한 라만 스펙트럼의 배경 잡음 제거 방법)

  • Park, Aaron;Baek, Sung-June
    • Smart Media Journal
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    • v.2 no.2
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    • pp.33-38
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    • 2013
  • In this paper, we investigated baseline estimation methods for remove background noise using Raman spectra from acute alcohol liver injury and acute ethanol-induced chronic liver fibrosis. Far the baseline estimation, we applied first derivative, linear programming and rolling ball method. Optimal input parameter of each method were determined by the training rate of MAP (maximum a posteriori probability) classifier. According to the experimental results, classification results baseline estimation with the rolling ball algorithm gave about 89.4%, which is very promising results for classification of acute alcohol liver injury and acute ethanol-induced chronic liver fibrosis. From these results, to determined the appropriate methods and parameters of baseline estimation impact on classification performance was confirmed.

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The Comparison of Characteristics in various Speaker Adaptation Methods (여러 화자 적응 방법들의 특성 비교)

  • 황영수
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1998.06e
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    • pp.339-342
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    • 1998
  • In this paper, we proposed various speaker adaptation methods and studied the performance of these methods. Methods which were studied in this paper are MAPE(Maximum A Posteriori Probability Estimation), ARTMAP. In order to evaluate the performance of these methods, we used Korean isolated digits as the experimental data, the hybrid speaker adaptation method, which unfied MAPE, linear spectral estimating and outpur probability of SCHMM, showed the better recognition result than those which performed other methods. And the method using ARTMAP showed the similar result to above hybrid method.

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Hybrid Iterative Detection Algorithm for MIMO Systems (다중 안테나 시스템을 위한 Hybrid Iterative 검출 기법)

  • Kim, Sang-Heon;Shin, Myeong-Cheol;Kim, Kyeong-Yeon;Lee, Chung-Yong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.44 no.4 s.316
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    • pp.117-122
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    • 2007
  • For multiple antenna systems, we consider the hybrid iterative detection of the maximum a posteriori probability(MAP) detection and the linear detection such as the minimum-mean-square-error(MMSE) filtering with soft cancelation. We devise methods to obtain both the lower complexity of the linear detection and the superior performance of the MAP detection. Using the a prior probability of the coded bit which is extrinsic of the outer decoder, we compute the threshold of grouping and determine the detection scheme symbol by symbol. Through the simulation results, it is shown that the proposed receiver obtains the superior performance to the MMSE detector and the lower complexity than the MAP detector.

An Implementation of Turbo -Code Decoder using Posteriori Probability Optimization (사후확률 최적화를 이용한 터보코드 복호기 구현)

  • Noh Jin-Soo;Rhee Kang-Hyeon
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.4 s.310
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    • pp.73-79
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    • 2006
  • Due to the powerful correcting performance, turbo codes have been adopted in many communication standards such as W-CDMA(Wideband Code Division Multiple Access), CDMA2000, etc., and implemented by hardware in many kind of fields. Although several hardware structures and improved algorithm have been proposed, these problems such as hardware area, operating speed and power consumption are still a major issue to be solved in practical implementations. In this paper, we designed the turbo-code decoder using MAX -SCALE operation derived from the posterior probability optimization. The proposed circuit has been measured their performance on Matlab and MaxPlusII and implemented on the FPGA As a result, when implementing the proposed algorithm on the FPGA, this circuit only occupies 616 logic elements. And comparing the performance with the MAP(Maxirnum a Posteriori) decoding algorithm, the operating speed was increased by about 40%(56.48MHz) and BER(Bit Error Rate) was increased by 6.12.

DISPARITY ESTIMATION/COMPENSATION OF MULTIPLE BASELINED STEREOGRAM USING MAXIMUM A POSTERIORI ALGORITHM

  • Sang-Hwa;Park, Jong-Il;Lee, Choong-Woong
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 1999.06a
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    • pp.49-56
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    • 1999
  • In this paper, the general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived. The generalized formula is implemented with the plane configuration model and applied to multiple baselined stereograms. The probabilistic plane configuration model consists of independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model reduces the computation and guarantees the discontinuity at the object boundary region. The similarity model preserves the continuity or the high correlation of disparity distribution. In addition, we propose a hierarchical scheme of disparity compensation in the application to multiple-view stereo images. According to the experiments, the derived formula and the proposed estimation algorithm outperformed other ones. The proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to O(n(D)) from O(n(D)4) of the generalized formula. And, the hierarchical scheme of disparity compensation with multiple-view stereos improves the performance without any additional overhead to the decoder.

Turbo Equalization using Belief Propagation (Belief Propagation을 이용한 터보 등화기)

  • Lee, Yun-Hee;Choi, Soo-Yong
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.281-282
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    • 2008
  • Turbo equalizers which use MAP (maximum a posteriori probability) equalizer or MMSE (minimum mean square error) equalizer have shown high performance and adoptability [1], [2]. In this paper, we show that the BP (belief propagation) algorithm can also be applied in equalizer and when it is connected with channel code, it can replace the MAP equalizer with similar complexity and performance.

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A Method of Feature Extraction on Micro-Raman Spectra for Classification of Neuro-degenerative Disorders (마이크로 라만 스펙트럼에서 퇴행성 뇌신경질환 분류를 위한 특징 추출 방법 연구)

  • Park, Aa-Ron;Baek, Sung-June
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.48 no.2
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    • pp.80-85
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    • 2011
  • Alzheimer's disease and Parkinson's disease are the most common neurodegenerative disorders. In this paper, we proposed a feature extraction method for classification of AD and PD based on micro-Raman spectra from platelet. The first step of the preprocessing is a simple smoothing followed by background elimination to the original spectra to make it easy to measure the intensity of the peaks. The last step of the preprocessing was peak alignment with the reference peak. After the inspection of the preprocessed spectra, we found that proportion of two peak intensity at 743 and $757cm^{-1}$ and peak intensity at 1248 and $1448cm^{-1}$ are the most discriminative features. Then we apply mapstd method for normalization. The method returned data with means to 0 and deviation to 1. With these three features, the classification result involving 263 spectra showed about 95.8% true classification in case of MAP(maximum a posteriori probability).