• 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.

• 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.

• 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.

• ETRI Journal
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• v.45 no.1
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• pp.138-149
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• 2023

In this study, the problem of blind signal separation for coprime planar arrays is investigated. For coprime planar arrays comprising two uniform rectangular subarrays, we link the signal separation to the tensor-based model called coupled canonical polyadic decomposition (CPD) and propose an improved coupled trilinear decomposition approach. The output data of coprime planar arrays are modeled as a coupled tensor set that can be further interpreted as a coupled CPD model, allowing a signal separation to be achieved using coupled trilinear alternating least squares (TALS). Furthermore, in the procedure of the coupled TALS, a Vandermonde structure enforcing approach is explicitly applied, which is shown to ensure fast convergence. The results of Monto Carlo simulations show that our proposed algorithm has the same separation accuracy as the basic coupled TALS but with a faster convergence speed.

• 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 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.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.416-418
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• 2006

In this paper, a new acoustic echo cancellation approach based on the DUET algorithm and scaling factor estimation is proposed to solve the scaling ambiguity in case of blind separation based acoustic echo cancellation in a noisy environment. In hands-free full-duplex communication system. acoustic noises picked up by the microphone are mixed with echo signal. For this reason, the echo cancellation system may provide poor performance. For that purpose, a degenerate unmixing estimation technique, adjusted in the time-frequency domain, is employed to separate undesired echo signals and noises. Also, since scaling and permutation ambiguities have not been solved in the blind source separation algorithm, kurtosis for the desired signal selection and a scaling factor estimation algorithm are utilized in this rarer for the separation of an echo signal. Simulation results demonstrate that the proposed approach yields better echo cancellation and noise reduction performances, compared with conventional methods.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.25 no.4E
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• pp.148-153
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• 2006

A new normalized MBD algorithm is presented for nonstationary convolutive mixtures and its properties/modifications are discussed in details. The proposed algorithm normalizes the signal spectrum in the frequency domain to provide faster stable convergence and improved separation without whitening effect. Modifications such as nonholonomic constraints and off-diagonal learning to the proposed algorithm are also discussed. Simulation results using a real-world recording confirm superior performanceof the proposed algorithm and its usefulness in real world applications.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.1149-1159
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• 2008

Blind source extraction (BSE) is a special class of blind source separation (BSS) methods, which only extracts one or a subset of the sources at a time. Based on the time delay of the desired signal, a simple but important extraction algorithm (simplified " BC algorithm")was presented by Barros and Cichocki. However, the performance of this method is not satisfying in some cases for which it only carries out the constrained minimization of the mean squared error. To overcome these drawbacks, ICA with reference (ICA-R) based approach, which considers the higher-order statistics of sources, is added as the second stage for further source extraction. Specifically, BC algorithm is exploited to roughly extract the desired signal. Then the extracted signal in the first stage, as the reference signal of ICA-R method, is further used to extract the desired sources as cleanly as possible. Simulations on synthetic data and real-world data show its validity and usefulness.