• ETRI Journal
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• v.39 no.1
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• pp.21-29
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• 2017

This paper addresses the problem of unsupervised speech separation based on robust non-negative matrix factorization (RNMF) with ${\beta}$-divergence, when neither speech nor noise training data is available beforehand. We propose a robust version of non-negative matrix factorization, inspired by the recently developed sparse and low-rank decomposition, in which the data matrix is decomposed into the sum of a low-rank matrix and a sparse matrix. Efficient multiplicative update rules to minimize the ${\beta}$-divergence-based cost function are derived. A convolutional extension of the proposed algorithm is also proposed, which considers the time dependency of the non-negative noise bases. Experimental speech separation results show that the proposed convolutional RNMF successfully separates the repeating time-varying spectral structures from the magnitude spectrum of the mixture, and does so without any prior training.

• The Journal of the Acoustical Society of Korea
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• v.31 no.5
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• pp.317-331
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• 2012

In this paper, we develop a multichannel blind source separation algorithm based on a beamspace transform and the multichannel non-negative matrix factorization (NMF) method. The NMF algorithm is a famous algorithm which is used to solve the source separation problems. In this paper, we consider a beamspace-time-frequency domain data model for multichannel NMF method, and enhance the conventional method using a beamspace transform. Our decomposition algorithm is applied to audio source separation, using a dataset from the international Signal Separation Evaluation Campaign 2010 (SiSEC 2010) for evaluation.

• Journal of Industrial and Engineering Chemistry
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• v.67
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• pp.156-163
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• 2018

High-performance mixed-matrix membranes that comprise both zeolitic imidazolate framework-8 (ZIF-8) and graphene oxide (GO) were synthesized with a solution casting technique to realize excellent $CO_2/CH_4$ separation. The incorporation of ZIF-8 nanocrystals alone in ODPA-TMPDA polyimide can be used to significantly enhance $CO_2$ permeability compared with that of pure ODPA-TMPDA. Meanwhile, the addition of a GO nanostack alone in ODPA-TMPDA contributes to improved $CO_2/CH_4$ selectivity. Hence, a composite membrane that contains both fillers displays significant enhancements in $CO_2$ permeability (up to 60%) and $CO_2/CH_4$ selectivity (up to 28%) compared with those of pure polymeric membrane. Furthermore, in contrast to the ZIF-8 mixed-matrix membrane, which showed decreased mechanical stability, it was found that the incorporation of GO could improve the mechanical strength of mixed-matrix membranes. Overall, the synergistic effects of the use of both fillers together are successfully demonstrated in this paper. Such significant improvements in the mixed-matrix membrane's $CO_2/CH_4$ separation performance and mechanical strength suggest a feasible and effective approach for potential biogas upgrading and natural gas purification.

• ETRI Journal
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• v.37 no.4
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• pp.780-786
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• 2015

This paper describes an audio source separation that is based on nonnegative matrix factorization (NMF) and expectation maximization (EM). For stable and highperformance separation, an effective auxiliary source separation that extracts source residuals and reprojects them onto proper sources is proposed by taking into account an ambiguous region among sources and a source's refinement. Specifically, an additional NMF (model) is designed for the ambiguous region - whose elements are not easily represented by any existing or predefined NMFs of the sources. The residual signal can be extracted by inserting the aforementioned model into the NMF-EM-based audio separation. Then, it is refined by the weighted parameters of the separation and reprojected onto the separated sources. Experimental results demonstrate that the proposed scheme (outlined above) is more stable and outperforms existing algorithms by, on average, 4.4 dB in terms of the source distortion ratio.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.93-98
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• 1991

An improved and flexible matrix algorithm for solving interlinked separation problems which is based on the homotopy continuation method has been developed. A flexible model of the interlinked stream in standardized matrix form and JACOBIAN generation I algorithm for homotopy continuation are suggested. Also DOF analysis is performed for easy-understanding of equation based simulation of complex column systems. The Algorithm is tested on several problems of interlinked separation processes and some of results are documented.

• Sleep Medicine and Psychophysiology
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• v.8 no.1
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• pp.67-71
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• 2001

Independent Component Analysis (ICA) is a blind source separation method using unsupervised learning and mutual information theory created in the late eighties and developed in the nineties. It has already succeeded in separating eye movement artifacts from human scalp EEG recording. Several characteristic sleep waves such as sleep spindle, K-complex, and positive occipital sharp transient of sleep (POSTS) can be recorded during sleep EEG recording. They are used as stage determining factors of sleep staging and might be reflections of unknown neural sources during sleep. We applied the ICA method to sleep EEG for sleep waves separation. Eighteen channel scalp longitudinal bipolar montage was used for the EEG recording. With the sampling rate of 256Hz, digital EEG data were converted into 18 by n matrix which was used as a original data matrix X. Independent source matrix U (18 by n) was obtained by independent component analysis method ($U=W{\timex}X$, where W is an 18 by 18 matrix obtained by ICA procedures). ICA was applied to the original EEG containing sleep spindle, K-complex, and POSTS. Among the 18 independent components, those containing characteristic shape of sleep waves could be identified. Each independent component was reconstructed into original montage by the product of inverse matrix of W (inv(W)) and U. The reconstructed EEG might be a separation of sleep waves without other components of original EEG matrix X. This result (might) demonstrates that characteristic sleep waves may be separated from original EEG of unknown mixed neural origins by the Independent Component Analysis (ICA) method.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.120-130
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• 2020

This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the 'Signal Separation Evaluation Campaign 2008 development dataset'. As a result, the improvement in most of the performance indicators was confirmed by utilizing the 'Blind Source Separation Eval toolbox', an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1827-1830
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• 2002

A matrix pencil method for source separation 〔3〕was shown to be an unbiased signal extractor in the presence of temporally white noise. Its efficiency and robustness lies in the fact that the method in 〔3〕 employs only time-delayed correlation matrices of the observation data, In this paper we stress out that the matrix pencil method might suffer from a numerical instability problem, be- cause the symmetric-definite pencil was not exploited. Moreover we present a simple method of constructing a symmetric-definite pencil so that the matrix pencil method is numerically stable.

• Membrane Journal
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• v.23 no.6
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• pp.432-438
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• 2013

Mixed-matrix membranes (MMMs) containing MIL-100(Fe), a MOF type, were fabricated in this study. MMMs up to 30 wt% MOF loading were prepared, and their gas permeabilities were tested. $H_2$, $CO_2$, $O_2$, $N_2$, and $CH_4$ gas permeabilities increased with the MOF loading, while $SF_6$, the largest kinetic diameter in this study, exhibited reduction of gas permeability with the loading. Ideal gas selectivity of $N_2/SF_6$ improved by 40% as compared with pure polyimide membrane, suggesting the proposed MMMs were suitable for $N_2/SF_6$ separation.

• Journal of KIISE:Software and Applications
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• v.35 no.7
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• pp.417-423
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• 2008

In this paper we present a probabilistic method for source separation in the case here each source has a certain temporal structure. We tackle the problem of source separation by maximum pseudo-likelihood estimation, representing the latent function which characterizes the temporal structure of each source by a random process with a Gaussian prior. The resulting pseudo-likelihood of the data is Gaussian, determined by a mixing matrix as well as by the predictive mean and covariance matrix that can easily be computed by Gaussian process (GP) regression. Gradient-based optimization is applied to estimate the demixing matrix through maximizing the log-pseudo-likelihood of the data. umerical experiments confirm the useful behavior of our method, compared to existing source separation methods.