• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.199-206
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• 2004

This paper proposes a hybrid independent component analysis(ICA) of fixed-point(FP) algorithm and robust algorithm. The FP algorithm is applied for improving the analysis speed and performance, and the robust algorithm is applied for preventing performance degradations by means of very small kurtosis and temporal correlations between components. And the adaptive adaptation of temporal correlations has been proposed for solving limits of the conventional robust algorithm dependent on the maximum time delay. The proposed ICA has been applied to the problems for separating the 4-mixed signals of 500 samples and 10-mixed images of $512\times512$pixels, respectively. The experimental results show that the proposed ICA has a characteristics of adaptively adapting the maximum time delay, and has a superior separation performances(speed, rate) to conventional FP-ICA and hybrid ICA of heuristic correlation. Especially, the proposed ICA gives the larger degree of improvement as the problem size increases.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.294-299
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• 2005

This paper presents the separation performance of the linearly mixed image signals with additive noises by using an independent component analyses(ICAs) of the fixed-point(FP) algorithm based on Newton and secant method, respectively. The Newton's FP-ICA uses the slope of objective function, and the secant's FP-ICA also uses the tangent line of objective function. The 2 kinds of ICA have been applied to the 2 dimensional 2-image with $512\times512$ pixels. Then Gaussian noise and Laplacian noise are added to the mixed images, respectively. The experimental results show that the Newton's FP-ICA has better the separation speed than secant FP-ICA and the secant's FP-ICA has also the better separation rate than Newton's FP-ICA. Especially, the Newton and secant method gives relatively larger improvement degree in separation speed and rate as the noise increases.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.813-820
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• 2004

This paper proposes a digital image watermarking which is robust to geometric distortions using an independent component analysis(ICA) of fixed-point(FP) algorithm based on secant method. The FP algorithm of secant method is applied for better performance in a separation time and rate, and ICA is applied to reject the prior knowledges for original image, key, and watermark such as locations and size, etc. The proposed method embeds the watermark into the spatial domain of original image The proposed watermarking technique has been applied to lena, key, and two watermarks(text and Gaussian noise) respectively. The simulation results show that the proposed method has higher speed and better rate for extracting the original images than the FP algorithm of Newton method. And the proposed method has a watermarking which is robust to geometric distortions such as resizing, rotation, and cropping. Especially, the watermark of images with Gaussian noise has better extraction performance than the watermark with text since Gaussian noise has lower correlation coefficient than the text to the original and key images. The watermarking of ICA doesn't require the prior knowledge for the original images.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.627-630
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• 2003

본 연구에서는 고정점 알고리즘의 독립성분분석과 원 신호의 시간적 상관성을 고려한 견실 알고리즘의 독립성분분석을 각각 이용하여 혼합지문영상을 분리하였다. 여기서 고정점 알고리즘은 뉴우턴법의 경신규칙을 이용함으로써 빠른 분리속도를 가진다. 견실 알고리즘은 2차적 통계성의 일괄처리 알고리즘으로 시간적 상관성과 낮은 kurtosis를 가진 영상분리에 효과적이다. 이들 기법들을 $256{\times}256$ 픽셀의 8개 지문으로부터 임의의 혼합행렬에 따라 발생되는 지문의 분리에 적용한 결과, 견실 알고리즘이 고정점 알고리즘의 독립성분분석에 비해 우수한 분리성능과 빠른 분리속도가 있음을 확인하였다.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.452-456
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• 2003

We discuss how to reduce the number of inverse matrix and its dimensions requested in MLLR framework for speaker adaptation. To find a smaller set of variables with less redundancy, we adapt PCA (principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible. The amount of additional computation when PCA or ICA is applied is as small as it can be disregarded. 10 components for ICA and 12 components for PCA represent similar performance with 36 components for ordinary MLLR framework. If dimension of SI model parameter is n, the amount of computation of inverse matrix in MLLR is proportioned to O(n⁴). So, compared with ordinary MLLR, the amount of total computation requested in speaker adaptation is reduced by about 1/81 in MLLR with PCA and 1/167 in MLLR with ICA.

• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.177-183
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• 2019

In this paper, we proposed an application method of ICA (Independent Component Analysis) to passive line array sonar to separate interferences from target signals in low frequency band and compared performance of three conventional ICA algorithms. Since the low frequency signals are received through larger bearing angles than other frequency bands, neighboring beam signals can be used to perform ICA as measurement signals of the ICA. We use three ICA algorithms such as Fast ICA, NNMF (Non-negative Matrix Factorization) and JADE (Joint Approximation Diagonalization of Eigen-matrices). Through experiments on real data obtained from passive line array sonar, it is verified that the interference can be separable from target signals by the suggested method and the JADE algorithm shows the best separation performance among the three algorithms.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.467-469
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• 1999

Independent component analysis (ICA)는 주어진 데이터를 통계적으로 독립인 요소들의 선형 결합으로 표시하는 통계학적 방법이다. ICA의 주요한 적용분야중의 하나는 source들의 선형 mixture로부터 어떠한 서전 정보도 없는 상태에서 원래의 통계학적 독립변수인 source를 복원하는 blind separation이다. ICA와 source separation을 위한 다양한 신경 학습 알고리듬이 제시되어왔다. ICA의 학습 알고리듬에서는 비선형 함수가 중요한 역할을 한다. 이 논문에서는 generalized 가우시안 prior를 도입하여 다양한 확률분포를 갖는 source들의 mixture를 분리하는 효율적인 source separation 알고리즘을 제시한다. 모의실험을 통하여 제안된 방법의 우수성을 살펴본다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.65-71
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• 2004

We improved the MLLR speaker adaptation algorithm with reduction of the order of HMM parameters using PCA(Principle Component Analysis) or ICA(Independent Component Analysis). To find a smaller set of variables with less redundancy, we adapt PCA(principal component analysis) and ICA(independent component analysis) that would give as good a representation as possible, minimize the correlations between data elements, and remove the axis with less covariance or higher-order statistical independencies. Ordinary MLLR algorithm needs more than 30 seconds adaptation data to represent higher word recognition rate of SD(Speaker Dependent) models than of SI(Speaker Independent) models, whereas proposed algorithm needs just more than 10 seconds adaptation data. 10 components for ICA and PCA represent similar performance with 36 components for ordinary MLLR framework. So, compared with ordinary MLLR algorithm, the amount of total computation requested in speaker adaptation is reduced by about 1/167 in proposed MLLR algorithm.

• 전자공학회논문지 IE
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• v.45 no.3
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• pp.20-26
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• 2008

Two independent component analysis(ICA) algorithms were applied for the recognition of speech signals corrupted by a car engine noise. Speech recognition was performed by hidden markov model(HMM) for the estimated signals and recognition rates were compared with those of orginal speech signals which are not corrupted. Two different ICA methods were applied for the estimation of speech signals, one of which is FastICA algorithm that maximizes negentropy, the other is information-maximization approach that maximizes the mutual information between inputs and outputs to give maximum independence among outputs. Word recognition rate for the Korean news sentences spoken by a male anchor is 87.85%, while there is 1.65% drop of performance on the average for the estimated speech signals by FastICA and 2.02% by information-maximization for the various signal to noise ratio(SNR). There is little difference between the methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.151-154
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• 2013

다수의 음원이 특정한 공간에 산재하고 있을 때, 그 중 특정 음원에 주목하면 다른 음원과 분리되어 특정 음원만 들리는 현상을 칵테일파티 현상이라고 한다. 심리적인 이 현상에 영감을 받아 음원을 분리하는 알고리즘이 만들어졌다. 이런 음원 분리방법을 Blind Source Separation(BSS) 이라고 하는데, 여러 신호가 섞이는 과정을 모르는 상태에서 음원을 분리한다는 뜻에서 Blind Source Separation 이라고 한다. BSS에 사용되는 알고리즘으로 주로 PCA, ICA이 있다. PCA는 2차원의 경우를, ICA는 그 이상의 고차원의 통계적 특성을 이용한다. 이에 본 논문은 PCA를 이용하여 두 음원을 분리하는 알고리즘을 구현하는데 역점을 두었다. PCA는 주로 음원보다는 이미지 신호 처리에 초점이 맞추어져 있지만, 음원 분리에 있어서도 충분한 성능을 보여주므로, ICA를 이용한 음원 분리 알고리즘과의 비교를 통하여 장, 단점을 알아보고 추후 PCA의 응용 가능성을 알아보았다.