• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.409-416
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• 2022

This paper relates to the blind noise separation method of time-delayed convolutive mixed signals. Since the mixed model of acoustic signals in a closed space is multi-channel, a convolutive blind signal separation method is applied and time-delayed data samples of the two microphone input signals is used. For signal separation, the mixing coefficient is calculated using an inverse model rather than directly calculating the separation coefficient, and the coefficient update is performed by repeated calculations based on secondary statistical properties to estimate the speech signal. Many simulations were performed to verify the performance of the proposed blind signal separation. As a result of the simulation, noise separation using this method operates safely regardless of convolutive mixing, and PESQ is improved by 0.3 points compared to the general adaptive FIR filter structure.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1191-1194
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• 1998

This paper presents a new computationally efficient adaptive algorithm for blind signal separation, which is able to recover the narrowband source signals in the presence of cochannel interference without a prior knowledge of array manifold. We derive a new blind signal separation algorithm using the Natural gradient 〔1〕from an information-theoretic approach. The resulting algorithm has the Bussgang property which has been widely used in blind equalization 〔12〕. Extensive computer simulation results comfirm the validity and high performance of the proposed algorithm.

• Journal of the Optical Society of Korea
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• v.12 no.3
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• pp.121-125
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• 2008

We address the problem of Blind Source Separation(BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis(ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images(two-dimensional signals).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.11
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• pp.1023-1030
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• 2014

Beam steering algorithms using array antenna are mainly used in such a manner as anti-jamming method. However, the performance is changed according to the position of signals despite the same number of signals and the same received power. In this paper, we analyzed the effect of the position relationship of the signals on the performance of the beam steering algorithms. Therefore, we defined 'signal separation' as the minimum angle of the interference signals and the desired signal, and analysed the relationship between the C/N0 performance of beam steering algorithms as LCMV/PM and the signal separation. For simulation, we set many GPS signals and jamming signals compared to the degrees of freedom. And changed the position and the height of the receiver in order to obtain various signal separation angles. In addition, we examined the effects of signal separation and JSR on the anti-jamming performance by applying the loss factors of the received power. Through the research, there is a tendency that the performance of beam steering algorithms is increased with the increase of the signal separation, and signal separation is more severely affecting on the anti-jamming performance compared to the number of signals and JSR.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.143-149
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• 2014

This paper is on the blind signal separation(BSS) method by the geometric method. To separate the signal sources, we use Hough transform and BSS. Hough transform is a geometric method which let us know the local informations of the signal. We find the orientations of signals by Hough transform and know the number of signal sources. When the number of sensors is more than the number of sources. the BSS algorithm can separate the mixtures well in the time domain. This algorithm has a good performance in converging fast. We had checked up the quality of the algorithm after separating the mixed signals. The results of simulations show that this BSS method has the abnormal waveforms due to unconverging coefficients in the beginning, and stably has the separated waveforms which almost equal to the sources in the most period.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.419-426
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• 2017

This paper describes an acoustic echo canceller with double-talk using BSS(: Blind Signal Separation) based on the geometric concept. The acoustic echo canceller may be deteriorated or diverged during the double-talk period. So we use the blind signal separation to detect the double talking by separating the near-end speech signal from the mixed microphone signal. In the closed reverberation environment, the blind signal separation extracts the near-end signal from unknown signals with the transformation and rotation based on the geometric concept. By this method, the acoustic echo canceller operates irrespective of double-talking. We verified performances of the proposed acoustic echo canceller by computer simulations. The results show that the acoustic echo canceller with this algorithm detects the double-talk periods thoroughly, and operates stably in the normal state without diverging of coefficients after ending the double-talking.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.2
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• pp.107-114
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• 2002

The problem of blind signal separation of independent sources consist in retrieving the source from the observation of unknown mixtures of unknown sources. In this paper, we propose a technique for blind signal separation that can extract original signals from their non-stationary mixtures observed in a ordinary room. The proposed method implements blind signal separation by minimizing a non-negative cost function that achieves the minimum when the second-order cross-correlation value of the observed signals becomes zero. The validity of the proposed method has been verified by a computer simulation and experiment that extracts two source signals from their mixtures observed in a normal room.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2434-2453
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• 2019

To improve the separation performance of time-frequency overlapped radar and communication signals from a single channel, this paper proposes an effective separation method based on an improved empirical wavelet transform (EWT) that introduces a fast boundary detection mechanism. The fast boundary detection mechanism can be regarded as a process of searching, difference optimization, and continuity detection of the important local minima in the Fourier spectrum that enables determination of the sub-band boundary and thus allows multiple signal components to be distinguished. An orthogonal empirical wavelet filter bank that was designed for signal adaptive reconstruction is then used to separate the input time-frequency overlapped signals. The experimental results show that if two source components are completely overlapped within the time domain and the spectrum overlap ratio is less than 60%, the average separation performance is improved by approximately 32.3% when compared with the classic EWT; the proposed method also improves the suitability for multiple frequency shift keying (MFSK) and reduces the algorithm complexity.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.224-229
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• 2004

In this paper, we propose a new Active Noise Control (ANC) system using a teacher forced Blind Source Separation (BSS) algorithm. The Blind Source Separation based on the Independent Component Analysis (ICA) separates the desired sound signal from the unwanted noise signal. In the proposed system, the BSS algorithm is used as a preprocessor of ANC system. Also, we develop a teacher forced BSS learning algorithm to enhance the performance of BSS. The teacher signal is obtained from the output signal of the ANC system. Computer experimental results show that the proposed ANC system in conjunction with the BSS algorithm effectively cancels only the ship engine noise signal from the linear and convolved mixtures with human voice.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.3-8
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• 1999

In this paper, we propose a new active noise control system that cancels the only noise signal from the mixture selectively. A blind source separation realized by a dynamic recurrent neural network is used as a preprocessor of the active noise control system and separates the desired signal and the noise signal. The active noise control system adaptively generates an anti-noise signal to remove the only noise signal separated by the blind source separation. Computer simulation results show that the proposed scheme is effective to construct a selective attention system.