• 한국음향학회지
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• 제27권1호
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• pp.26-32
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• 2008

UBSS (under-determined blind source separation)는 BMMR (blind mixing matrix recovery) 과정과 BSR (blind source recovery) 과정으로 구분된다. 일반적으로 이 두 과정은 취득된 데이터의 sparseness를 이용하여 수행되는데, 얼마나 sparseness를 정확히 측정하느냐에 따라 그 성능이 좌우된다. 본 논문에서는 Gini 계수를 이용한 sparseness의 측정 방법을 BSR 과정에 도입하여, $l_1$-노름, $l_q$-노름과 쌍곡탄젠트 (hyperbolic tangent)를 이용하는 측정 방법들과 비교하였으며, 보다 정확한 sparseness 측정과 향상된 BSR 성능을 획득하였다. 이는 컴퓨터 모의 실험을 통하여 검증되었다.

• 전기학회논문지
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• 제57권8호
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• pp.1454-1463
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• 2008

Blind source separation by independent component analysis (ICA) has applied in signal processing, telecommunication, and image processing to recover unknown original source signals from mutually independent observation signals. Neural networks are learned to estimate the original signals by unsupervised learning algorithm. Because the outputs of the neural networks which yield original source signals are mutually independent, then mutual information is zero. This is equivalent to minimizing the Kullback-Leibler convergence between probability density function and the corresponding factorial distribution of the output in neural networks. In this paper, we present a learning algorithm using information theory and higher order statistics to solve problem of blind source separation. For computer simulation two deterministic signals and a Gaussian noise are used as original source signals. We also test the proposed algorithm by applying it to several discrete images.

• The Journal of the Acoustical Society of Korea
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• 제25권4E호
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• pp.142-147
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• 2006

Blind signal separation is known as a powerful tool for enhancing noisy speech in many real world environments. In this paper, it is demonstrated that the performance of blind signal separation can be further improved by combining with a null beamformer (NBF). Cascading the blind source separation with null beamforming is equivalent to the decomposition of the received signals into the direct parts and reverberant parts. Investigation of beam patterns of the null beamformer and blind signal separation reveals that directional null of NBF reduces mainly direct parts of the unwanted signals whereas blind signal separation reduces reverberant parts. Further, it is shown that the decomposition of received signals can be exploited to solve the local stability problem. Therefore, faster and improved separation can be obtained by removing the direct parts first by null beamforming. Simulation results using real office recordings confirm the expectation.

• Journal of the Optical Society of Korea
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• 제12권3호
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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).

• Structural Engineering and Mechanics
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• 제78권3호
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• pp.369-378
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• 2021

This paper reviewed a few output-only system identification algorithms and identified the shortcomings of those popular blind source separation methods. To address the issues such as less sensors than the targeted modal modes (under-determinate problem), repeated natural frequencies as well as systems with complex mode shapes, this paper proposed a complex wavelet modified second order blind identification method (CWMSOBI) by transforming the time domain problem into time-frequency domain. The wavelet coefficients with different dominant frequencies can be used to address the under-determinate problem, while complex mode shapes are addressed by introducing the complex wavelet transformation. Numerical simulations with both high and low signal-to-noise ratios validate that CWMSOBI can overcome the above-mentioned issues while obtaining more accurate identified results than other blind identification methods.

• Structural Monitoring and Maintenance
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• 제1권1호
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• pp.69-87
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• 2014

A new output only modal analysis method is developed in this paper. This method uses continuous wavelet transform to modify a popular blind source separation algorithm, second order blind identification (SOBI). The wavelet modified SOBI (WMSOBI) method replaces original time domain signal with selected time-frequency domain wavelet coefficients, which overcomes the shortcomings of SOBI. Both numerical and experimental studies on bridge models are carried out when there are limited number of sensors. Identified modal properties from WMSOBI are analyzed and compared with fast Fourier transform (FFT), SOBI and eigensystem realization algorithm (ERA). The comparison shows WMSOBI can identify as many results as FFT and ERA. Further case study of structural health monitoring (SHM) on an arch bridge verifies the capability to detect damages by combining WMSOBI with incomplete flexibility difference method.

• 한국음향학회지
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• 제18권6호
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• pp.3-8
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• 1999

본 연구에서는 신호원에 대한 사전 정보 없이 혼합된 신호로부터 잡음 신호만을 선택적으로 제거할 수 있는 새로운 형태의 능동 소음 제거기(Active noise canceller)를 제안한다. 음성신호와 같은 동특성을 갖는 신호의 분리에 효과적으로 사용되는 동적 재귀 신경망(Dynamic recurrent neural network)을 원하는 신호원에 섞인 잡음신호를 분리하여 선택적으로 제거하기 위한 능동 소음 제거기의 전처리기로 이용한다. 능동 소음 제거기는 분리된 잡음 신호에 대한 역위상 신호를 적응적으로 발생함으로써 특정 위치에서 원하는 신호만을 선택적으로 남길 수 있도록 한다. 컴퓨터 시뮬레이션을 통하여 제안된 시스템이 선택적인 소음제거에 효과적임을 보인다.

• 말소리와 음성과학
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• 제5권4호
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• pp.211-216
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• 2013

The IVA(Independent Vector Analysis) is a well-known FD-ICA method used to solve the frequency permutation problem. It generally works quite well for blind source separation problems, but still needs some improvements in the frequency bin permutation problem. This paper proposes a post-processing method which can improve the source separation performance with the IVA by fixing the remaining frequency permutation problem. The proposed method makes use of the correlation coefficient of power ratio between frequency bins for separated signals with the IVA-based 2-channel source separation. Experimental results verified that the proposed method could fix the remaining frequency permutation problem in the IVA and improve the speech quality of the separated signals.

• Journal of applied mathematics & informatics
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• 제26권5_6호
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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.

• The Journal of the Acoustical Society of Korea
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• 제23권2E호
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• pp.56-67
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• 2004

Despite abundant research outcomes of blind source separation (BSS) in many types of simulated environments, their performances are still not satisfactory to be applied to the real environments. The major obstacle may seem the finite filter length of the assumed mixing model and the nonlinear sensor noises. This paper presents a two-step speech enhancement method with multiple microphone inputs. The first step performs a frequency-domain BSS algorithm to produce multiple outputs without any prior knowledge of the mixed source signals. The second step further removes the remaining cross-channel interference by a spectral cancellation approach using a probabilistic source absence/presence detection technique. The desired primary source is detected every frame of the signal, and the secondary source is estimated in the power spectral domain using the other BSS output as a reference interfering source. Then the estimated secondary source is subtracted to reduce the cross-channel interference. Our experimental results show good separation enhancement performances on the real recordings of speech and music signals compared to the conventional BSS methods.