• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2013년도 하계학술대회
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• pp.60-63
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• 2013

In this research, a mono-to-stereo upmixing algorithm based on music source separation is proposed. For the upmixing, a harmonic and percussive separation for jazz music is implemented. Then, the sources are re-panned by equalizing the loudness of left and right sides of listeners in the one proposed approach. In the other approach, the harmonic sources are spread by a decorrelator while the percussive sources are panned to the center. In the experiments, the re-panning algorithm showed advanced performance in terms of localization and timbral quality.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권7호
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• pp.399-406
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• 2000

We propose an nonlinear demodulation algorithm for undersampled multicomponent AM(Amplitude Modulation), FM(Frequency Modulation) and AM-FM signals. First, we derive respectively undersampling frequency of the AM, FM and AM-FM using undersampling scheme, and separate respectively monocomponent signals from multicomponent signals using periodic algebraic separation algorithm. In this case augmented separation matrix is very regular and sparse, it has a special structure. The proposed demodulation algorithm detects respectively message signals of the IA(Instantaneous Amplitude) and IF(Instantaneous Frequency) from descrete monocomponent AM, FM and AM-FM signals with an undersampling frequency to be controllable. Verifying the RMS(Root Mean Squares) errors of the detected signals, we show that the performance is excellent.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.299-300
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• 2008

In this paper, we propose Adaptive Group Separation(AGS) algorithm for efficient RFID system. AGS algorithm determines the optimized initial prefix size m, and divides the group of ��$2^m$. A reader requests the group and searches the tag ID. If a tag collision occurred, reader adds a one bit, '0' or '1' at first bit of collision point. As a result, we observe that transmitted data bits and the recognition time are decreased.

• 디지털산업정보학회논문지
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• 제10권3호
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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.

• 한국산업융합학회 논문집
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• 제7권1호
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• pp.41-46
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• 2004

This paper proposes a separation of mixed signals by using the robust independent component analysis(RICA) based on neural networks. RICA is based on the temporal correlations and the second order statistics of signal. This method e is applied for improving the analysis rate and speed in which the sources have very small or zero kurtosis. The proposed method has been applied for separating the 10 mixed finger prints of $256{\times}256$-pixel and the 4 mixed images of $512{\times}512$-pixel, respectively. The simulation results show that RICA has the separating rate and speed better than those using the conventional FP algorithm based on Newton method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.29-30
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• 2007

In this paper, we propose Group Separation(GS) algorithm for RFID tag recognition. In GS algorithm, reader calculates tag ID by collision point, stores memory with the collision table. And reader classifies according to total number of tag ID's 1, requests each group. If tag comes into collision with the other tag, reader searches tag ID in collision table. As a result, we observes that transmitted data rate, the recognition time is decreased.

• 한국정밀공학회지
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• 제26권5호
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• pp.72-78
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• 2009

We propose the separation algorithm to simultaneously measure two-dimensional refractive index distribution and thickness profile of transparent samples using three wavelengths. The optical system was based on the Mach-zehnder interferometer with LD (Laser Diode)-based multi-wavelength sources. A LCR (Liquid Crystal Retarder) was used to obtain interference images at four phase states and then the optical phase of the object is calculated by four-bucket algorithm. Experimental results with a glass rod are provided at the different wavelengths of 635nm, 660nm and 675nm. The refractive indices of the sample are distributed with accuracy of less than 0.0005 and the thickness profile of sample was cylindrical type. This result demonstrates that it is possible to separate refractive index distribution and thickness profile of samples in two dimensions using the proposed algorithm.

• ETRI Journal
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• 제35권2호
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• pp.193-199
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• 2013

This paper addresses nonnegative independent component analysis (NICA), with the aim to realize the blind separation of nonnegative well-grounded independent source signals, which arises in many practical applications but is hardly ever explored. Recently, Bertrand and Moonen presented a multiplicative NICA (M-NICA) algorithm using multiplicative update and subspace projection. Based on the principle of the mutual correlation minimization, we propose another novel cost function to evaluate the diagonalization level of the correlation matrix, and apply the multiplicative exponentiated gradient (EG) descent update to it to maintain nonnegativity. An efficient approach referred to as the EG-NICA algorithm is derived and its validity is confirmed by numerous simulations conducted on different types of source signals. Results show that the separation performance of the proposed EG-NICA algorithm is superior to that of the previous M-NICA algorithm, with a better unmixing accuracy. In addition, its convergence speed is adjustable by an appropriate user-defined learning rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2574-2587
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• 2019

Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.

• The Journal of the Acoustical Society of Korea
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• 제27권1E호
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• pp.17-24
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• 2008

Blind speech separation (BSS) is well-known as a powerful technique for speech enhancement in many real world environments. In this paper, we propose a new application of BSS - acoustic echo cancellation (AEC) in a car environment. For this purpose, we develop a block-online BSS algorithm which provides robust separation than a batch version in changing environments with moving speakers. Simulation results using real world recordings show that the block-online BSS algorithm is very robust to speaker movement. When combined with AEC, simulation results using real audio recording in a car confirm the expectation that BSS improves double talk detection and echo suppression.