• 전자공학회논문지 IE
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• v.49 no.2
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• pp.24-29
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• 2012

The sounds of exhaust emissions of automobiles are independent sound sources which are nothing to do with voices. We have no information for the sources of voices and exhaust sounds. Accordingly, Independent Component Analysis which is one of the Blind Source Separaton methods was used to segregate two source signals from each mixed signals. Maximum Likelyhood Estimation was applied to the signals came through the stereo microphone to segregate the two source signals toward the maximization of independence. Since there is no clue to find whether it is speech signal or not, the coefficients of the slope was calculated by the autocovariances of the signals in frequcency domain. Noise remover for speech signals was implemented by coupling the two algorithms.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.6
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• pp.498-503
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• 2012

Independent component analysis (ICA) technique based on statistical independency of the signals is known as suitable to identify the source signals by measuring and separating mixed signals through transfer paths and has successfully applied in the field of medical care, communications and so forth. In this study, the ICA technique is introduced for the identification of excitation sources from measured vibration signals of structures, which can be done by evaluating negentropy of centered and whitened vibration signals and correlation of separated signals. To validate the method, numerical analyses are carried out for a plate and a cylinder structure. The results show that the method can be applied efficiently to source identification of complex structures. Nevertheless, additional studies would be required to complement problems of occasional inaccuracy.

• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.269-274
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• 2004

Noisy speech recognition is one of most important problems in speech recognition. In this paper, a method which efficiently removes the mixed noise with speech, is proposed. The proposed method is based on the ICA to separate the mixed noise. ICA(Independent component analysis) is a signal processing technique, whose goal is to express a set of random variables as linear combinations of components that are statistically as independent from each other as possible.

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.58-63
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• 2015

Due to GPS signal's vulnerability of jamming attack, various enhancement techniques are needed. Among variety of techniques, we focused on GPS receiver's anti-jamming techniques. There are many anti-jamming methods at GPS receivers which include filtering methods in time domain, frequency domain and space domain. However, these methods are ineffective to signals, which include both jamming and noise. To solve the problem, this paper proposes a jamming separation scheme by using a BSS method in a jamming environment. As separated GPS signals include noise after the jamming separation method, it is difficult to receive accurate GPS signals. For this reason, this paper also proposes a wavelet de-noising method to effectively eliminate noise. Experimental results of this paper are based on a real field test data of an integrated GPS/QZSS/Wi-Fi positioning system. At the end, the simulation result demonstrates its superiority by showing improved positioning accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.164-167
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• 2012

The technology which is used for segregating voices signals from exhaust noise signals of a car is very practical one to realize the interfaces between men and machines using voices. The voice signals contaminated by exhaust noise signal of a car was separated by canonical correlation ananysis(CCA) in an environment which does not guarantee the independence between signals and have prior informations. Rearrangement for the input signals is important in CCA. CCA was studied and segragation between source signals were performed by CCA through rearrangements of each of signals. It is possible to apply the technique to various signals since it is also possible to use CCA to the signals which are not independent.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.838-842
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• 2007

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.10
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• pp.48-58
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• 1998

We present a method that can recognize face images using independent component analysis that is used mainly for blind sources separation in signal processing. We assumed that a face image can be expressed as the sum of a set of statistically independent feature images, which was obtained by using independent component analysis. Face recognition was peformed by projecting the input image to the feature image space and then by comparing its projection components with those of stored reference images. We carried out face recognition experiments with a database that consists of various varied face images (total 400 varied facial images collected from 10 per person) and compared the performance of our method with that of the eigenface method based on principal component analysis. The presented method gave better results of recognition rate than the eigenface method did, and showed robustness to the random noise added in the input facial images.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.107-114
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• 2012

In this paper, the concerned existing blind equalizer convergence rate and residual inter-symbol interference using constellation reduced and cost function by separation the real part and an imaginary part, the dual structure CR-CMA(constellation Reduction CMA). The CMA methed compensates amplitude but does no compensate phase, On the other hand, The CMA method compensates both the amplitude and the phase but it has the convergence rate problem, and the MCMA method is a way to solve the phase problem of CMA method compensates both the amplitude and the phase after respectively calculating the real part and imaginary part components. Proposal a new method that the dual structure of CR-CMA, the cost function and error function and respectively calculating the real part and imaginary part components can advantages by improving the CMA and the MCMA algorithms so that the amplitude and phase retrieval and constellation reduce the residual ISI and faster convergence rate and performance is good SER (Symbol Error Ratio) was confirmed by computer simulations.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.6
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• pp.22-31
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• 2000

In this paper, we propose a method for removing noise components in the feature extraction process for robust speech recognition. This method is based on blind separation using independent component analysis (ICA). Given two noisy speech recordings the algorithm linearly separates speech from the unwanted noise signal. To apply ICA as closely as possible to the feature level for recognition, a new spectral analysis is presented. It modifies the computation of band energies by previously averaging out fast Fourier transform (FFT) points in several divided ranges within one met-scaled band. The simple analysis using sample variances of band energies of speech and noise, and recognition experiments showed its noise robustness. For noisy speech signals recorded in real environments, the proposed method which applies ICA to the new spectral analysis improved the recognition performances to a considerable extent, and was particularly effective for low signal-to-noise ratios (SNRs). This method gives some insights into applying ICA to feature levels and appears useful for robust speech recognition.