• 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.

• 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.

• 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.

• 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.

• 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).

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 10월 학술대회지
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• pp.97-100
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• 2003

This paper addresses a method of convolutive source separation that based on SEONS (Second Order Nonstationary Source Separation) [1] that was originally developed for blind separation of instantaneous mixtures using nonstationarity. In order to tackle this problem, we transform the convolutive BSS problem into multiple short-term instantaneous problems in the frequency domain and separated the instantaneous mixtures in every frequency bin. Moreover, we also employ a H infinity filtering technique in order to reduce the sensor noise effect. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach and compare its performances with existing methods.

• 한국전자파학회논문지
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• 제13권2호
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• pp.107-114
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• 2002

미지의 신호원들의z 합성으로부터 관측된 신호만을 이용하여 통계적으로 독립인 원신호를 추출하는 문제를 블라인드 신호분리라 한다. 본 논문에서는 보통의 실내에서 얻어진 비정상(non-stationary) 합성신호로부터 원신호론 추출해내는 블라인드 신호분리 기법을 제안한다. 제안된 기법은 관측 신호들 간의 이타 상호상관 값이 제로가 될 때만 최소값을 가지는 비용함수를 최소화시키는 방식으로 블라인드 신호분리를 구현한다. 제안된 기법의 유효성을 컴퓨터 시뮬레이션과 보통의 실내에서 관측된 2개의 합성신호로부터 2개의 원신호를 추출해내는 실험을 통하여 증명한다.

• 디지털산업정보학회논문지
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• 제10권1호
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• pp.131-138
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• 2014

This paper is on a stereo acoustic echo canceller with the blind signal separation for post processing. The convergence speed of the stereo acoustic echo canceller is deteriorated due to mixing two residual signals which are update signals of each echo canceller. To solve this problem, we are to use the blind signal separation(BSS) method separating the mixed signals after the echo cancellers. The blind signal separation method can extracts the source signals by means of the iterative computations with two input signals. We had verified performances of the proposed acoustic echo canceller for stereo through simulations. The results of simulations show that the acoustic echo canceller for stereo using this algorithm operates stably without divergence in the normal state. And, when the speech signals were inputted, this echo canceller achieved about 2dB higher ERLE with the BSS post processing method than without this method. This stereo echo canceller showed the best performance in the case of inputting the real voice signal.

• 디지털산업정보학회논문지
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• 제8권3호
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• pp.125-131
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• 2012

This paper is on a stereo acoustic echo canceller with the blind signal separation. The convergence speed of the stereo acoustic echo canceller is deteriorated due to mixing two residual signals in the update signal of each echo canceller. To solve this problem, we are to use the blind signal separation(BSS) method separating the mixed signals. The blind signal separation method can extracts the source signals by means of the iterative computations with two input signals. We had verified performances of the proposed acoustic echo canceller for stereo through simulations. The results of simulations show that the acoustic echo canceller for stereo using this algorithm operates stably without divergence in the normal state. And, when the speech signals were inputted, this echo canceller achieved about 3dB higher ERLE in the case of using the BSS algorithm than the case of not using the BSS algorithm. But this echo canceller didn't get good performances in the case of inputting the white noises as stereo signals.

• 한국음향학회지
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• 제39권2호
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• pp.120-130
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• 2020

본 논문은 블라인드 소스 분리 분야에서 널리 사용되는 멀티채널 비음수 행렬 분해 기법의 단점을 개선하여 미결정 복잡한 혼합 환경에서 문제를 해결한다. 공간 공분산 행렬에 기반을 둔 기존의 연구들에서, 단일 채널의 파워게인 및 상관관계와 같은 값으로 구성된 행렬의 각 요소는 높은 분산으로 인해 분리된 소스의 품질을 저하시키는 경향이 있다. 이 논문에서는 추정된 소스들을 효과적으로 클러스터링하기 위해 레벨 및 주파수 정규화를 수행한다. 따라서 새로운 공간 공분산 행렬 및 효과적인 클러스터 쌍별 거리함수를 제안한다. 본 논문에서는 제안된 행렬을 공간 모델의 초기화에 활용하여 공간 모델의 향상된 추정과 이를 바탕으로 상향식 접근법에서의 계층적 응집 클러스터링에 활용함으로써 분리된 음원의 품질을 향상시켰다. 제안된 알고리즘은 'Signal Separation Evaluation Campaign 2008 development dataset'을 활용하여 실험을 하였다. 그 결과 객관적인 소스 분리 품질 검증 도구인 'Blind Source Separation Eval toolbox'를 활용하여 대부분의 성능향상지표에서의 향상을 확인하였으며, 특히 대표적인 수치인 SDR의 1 dB ~ 3.5 dB 정도의 성능우위를 검증하였다.