• 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.

• Journal of KIEE
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• v.11 no.1
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• pp.1-7
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• 2001

Various methods for blind source separation (BSS) are based on independent component analysis (ICA) which can be viewed as a nonlinear extension of principal component analysis (PCA). Most existing ICA methods require certain nonlinear functions (which leads to higher-order statistics) depending on the probability distributions of sources, whereas PCA is a linear learning method based on second-order statistics. In this paper we show that the PCA can be applied to the task of BBS, provided that source are spatially uncorrelated but temporally correlated. Since the resulting method is based on only second-order statistics, it avoids the nonlinear function and is able to separate mixtures of several colored Gaussian sources, in contrast to the conventional ICA methods.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.91-103
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• 2022

Determination of sound source characteristics such as: sound volume, direction and distance to the source is one of the important techniques for unmanned systems like autonomous vehicles, robot systems and AI speakers. There are multiple methods of determining the direction and distance to the sound source, e.g., using a radar, a rider, an ultrasonic wave and a RF signal with a sound. These methods require the transmission of signals and cannot accurately identify sound sources generated in the obstructed region due to obstacles. In this paper, we have implemented and evaluated a method of detecting and identifying the sound in the audible frequency band by a method of recognizing the volume, direction, and distance to the sound source that is generated in the periphery including the invisible region. A cross-shaped based sound source recognition algorithm, which is mainly used for identifying a sound source, can measure the volume and locate the direction of the sound source, but the method has a problem with "blind spots". In addition, a serious limitation for this type of algorithm is lack of capability to determine the distance to the sound source. In order to overcome the limitations of this existing method, we propose a QRAS-based algorithm that uses rectangular-shaped technology. This method can determine the volume, direction, and distance to the sound source, which is an improvement over the cross-shaped based algorithm. The QRAS-based algorithm for the OSSD uses 6 AITDs derived from four microphones which are deployed in a rectangular-shaped configuration. The QRAS-based algorithm can solve existing problems of the cross-shaped based algorithms like blind spots, and it can determine the distance to the sound source. Experiments have demonstrated that the proposed QRAS-based algorithm for OSSD can reliably determine sound volume along with direction and distance to the sound source, which avoiding blind spots.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.839-845
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• 2003

This paper introduces multichannel blind source separation (BSS) and multichannel blind deconvolution (MBD) based on higher order statistics of signals from convolutive mixtures. In particular, we are concerned with the case that the number of inputs is the same as the number of outputs. Simulations for two input two output cases are carried out and their performances are assessed. One of the major applications of those sequential algorithms (BSS and MBD) is demonstrated through the fault signal detection from only a single measurement of rotating machine, which offers a certain degree of practicability in the engineering field such as machine health monitoring or condition monitoring.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.753-760
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• 2005

This paper introduces a variable step-size adaptive algorithm for blind source separation. From the frequency characteristics of mixed input signals, we need to adjust the convergence speed regularly in each frequency bin. This algorithm varies a step-size according to the magnitude of input at each frequency bin. This guarantee of the regular convergence in each frequency bin would become more efficient in separation performances than conventional fixed step-size FDICA. Computer simulation results show the improvement of about 5 dB in signal to interference ratio (SIR) and the better separation quality.

• Journal of Communications and Networks
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• v.14 no.4
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• pp.410-417
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• 2012

One of the premier mechanisms used in extracting unobserved signals from observed mixtures in signal processing is employing a blind source separation (BSS) algorithm. Orthogonal frequency division multiplexing (OFDM) techniques are playing a prominent role in the sphere of multicarrier communication. A set of remedial solutions taken to mitigate deteriorative effects caused within the air interface of OFDM transmission with aid of BSS schemes is presented. Four energy functions are used in deriving the filter coefficients. Energy criterion functions to be optimized and the performance is justified. These functions together with iterative fixed point rule for receive signal are used in determining the filter coefficients. Time correlation properties of the channel are taken advantage for BSS. It is tried to remove colored noise and jamming components from themixture at the receiver. Themethod is tested in a slow fading channel with a receiver containing equal gain combining to treat the channel state information values. The importance is that, these are quite low computational complexity mechanisms.

• 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.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.585-590
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• 2004

Many machine failures are not detected well in advance due to the masking of background noise and attenuation of the source signal through the transmission mediums. Advanced signal processing techniques using adaptive filters and higher order statistics have been attempted to extract the source signal from the measured data at the machine surface. In this paper, blind deconvolution using the eigenvector algorithm (EVA) technique is used to recover a damaged bearing signal using only the measured signal at the machine surface. A damaged bearing signal corrupted by noise with varying signal-to-noise (s/n) was used to determine the effectiveness of the technique in detecting an incipient signal and the optimum choice of filter length. The results show that the technique is effective in detecting the source signal with an s/n ratio as low as 0.21, but requires a relatively large filter length.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2E
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• pp.54-60
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• 2005

A new multichannel blind deconvolution algorithm is proposed for speech mixtures. It employs unidirectional filters and normalization of gradient terms in the frequency domain. The proposed algorithm is shown to be approximately nonholonomic. Thus it provides improved convergence and separation performances without whitening effect for nonstationary sources such as speech and audio signals. Simulations using real world recordings confirm superior performances over existing algorithms and its usefulness for real applications.