The Experts below are selected from a list of 18000 Experts worldwide ranked by ideXlab platform
Laurenz Wiskott - One of the best experts on this subject based on the ideXlab platform.
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an extension of slow feature analysis for nonlinear Blind Source Separation
Journal of Machine Learning Research, 2014Co-Authors: Henning Sprekeler, Tiziano Zito, Laurenz WiskottAbstract:We present and test an extension of slow feature analysis as a novel approach to nonlinear Blind Source Separation. The algorithm relies on temporal correlations and iteratively reconstructs a set of statistically independent Sources from arbitrary nonlinear instantaneous mixtures. Simulations show that it is able to invert a complicated nonlinear mixture of two audio signals with a high reliability. The algorithm is based on a mathematical analysis of slow feature analysis for the case of input data that are generated from statistically independent Sources.
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Independent Slow Feature Analysis and Nonlinear Blind Source Separation
Neural Computation, 2007Co-Authors: Tobias Blaschke, Tiziano Zito, Laurenz WiskottAbstract:In the linear case, statistical independence is a sufficient criterion for performing Blind Source Separation. In the nonlinear case, however, it leaves an ambiguity in the solutions that has to be resolved by additional criteria. Here we argue that temporal slowness complements statistical independence well and that a combination of the two leads to unique solutions of the nonlinear Blind Source Separation problem. The algorithm we present is a combination of second-order independent component analysis and slow feature analysis and is referred to as independent slow feature analysis. Its performance is demonstrated on nonlinearly mixed music data. We conclude that slowness is indeed a useful complement to statistical independence but that time-delayed second-order moments are only a weak measure of statistical independence.
Shoji Makino - One of the best experts on this subject based on the ideXlab platform.
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frequency domain Blind Source Separation
Blind Speech Separation, 2007Co-Authors: Shoji Makino, Hiroshi Sawada, Shoko ArakiAbstract:This chapter discusses the frequency-domain approach to the Blind Source Separation (BSS) of convolutively mixed acoustic signals. In this approach, independent component analysis (ICA) is employed in each frequency bin to calculate the frequency responses of Separation filters. Since convolutive mixtures in the time domain can be approximated as multiple instantaneous mixtures in the frequency domain, the advantage of this approach is that ICA is applied just for instantaneous mixtures, which is very simple. However, the permutation ambiguity of ICA solutions then becomes a problem. This chapter mainly deals with a method for solving the permutation problem. The method utilizes the Source location information that can be estimated from the ICA solutions. We also discuss other important topics for frequency-domain BSS, such as complex-valued ICA, scaling alignment and spectral smoothing. To show the effectiveness of this frequency-domain approach, we report experimental results for separating up to four Sources with a 4-element linear array, and also six Sources with an 8-element planar array.
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Blind Source Separation of convolutive mixtures of speech in frequency domain
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2005Co-Authors: Shoji Makino, Ryo Mukai, Hiroshi Sawada, Shoko ArakiAbstract:This paper overviews a total solution for frequency-domain Blind Source Separation (BSS) of convolutive mixtures of audio signals, especially speech. Frequency-domain BSS performs independent component analysis (ICA) in each frequency bin, and this is more efficient than time-domain BSS. We describe a sophisticated total solution for frequency-domain BSS, including permutation, scaling, circularity, and complex activation function solutions. Experimental results of 2 × 2, 3 × 3, 4 × 4, 6 × 8, and 2 × 2 (moving Sources), (#Sources × #microphones) in a room are promising.
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array geometry arrangement for frequency domain Blind Source Separation
2003Co-Authors: Ryo Mukai, Hiroshi Sawada, Shoko Araki, Shoji MakinoAbstract:In this paper, we propose a method for solving the permutation problem of frequency domain Blind Source Separation (BSS) when the number of Source signals is large, and the potential Source locations are omnidirectional. Geometric information such as direction of arrival is helpful for solving the permutation problem, but the information becomes more uncertain as the number of Source signals increases. When we use a linear microphone array, we cannot obtain reliable geometric information due to the ambiguity and sensitivity inherent to the array geometry. We propose a combination of small and large spacing microphone pairs that have various axis directions. Experimental results show that the proposed method can separate a mixture of speech signals that come from various directions, even when some come from the same direction.
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polar coordinate based nonlinear function for frequency domain Blind Source Separation
International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Hiroshi Sawada, Ryo Mukai, Shoko Araki, Shoji MakinoAbstract:This paper presents a new type of nonlinear function for independent component analysis to process complex-valued signals, which is used in frequency-domain Blind Source Separation. The new function is based on the polar coordinates of a complex number, whereas the conventional one is based on the Cartesian coordinates. The new function is derived from the probability density function of frequency-domain signals that are assumed to be independent of the phase. We show that the difference between the two types of functions is in the assumed densities of independent components. Experimental results for separating speech signals show that the new nonlinear function behaves better than the conventional one.
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a polar coordinate based activation function for frequency domain Blind Source Separation
2001Co-Authors: Hiroshi Sawada, Ryo Mukai, Shoko Araki, Shoji MakinoAbstract:This paper presents a new activation function for an ICA algorithm to process complex-valued signals, which is used in frequency domain Blind Source Separation. The new activation function is based on the polar coordinates of a complex number, whereas the conventional one is based on the Cartesian coordinates of a complex number and calculates the real part and imaginary part separately. The new activation function eliminates an undesirable constraint occurred by the conventional function. In experiments for separating speech signals in a reverberant environment, we obtained improved SNRs by using the new activation function.
Yong Xiang - One of the best experts on this subject based on the ideXlab platform.
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Underdetermined Blind Source Separation Using Sparse Coding
IEEE Transactions on Neural Networks, 2016Co-Authors: Liangli Zhen, Yong Xiang, Dezhong Peng, Zhang Yi, Peng ChenAbstract:In an underdetermined mixture system with $n$ unknown Sources, it is a challenging task to separate these Sources from their $m$ observed mixture signals, where $m . By exploiting the technique of sparse coding, we propose an effective approach to discover some 1-D subspaces from the set consisting of all the time-frequency (TF) representation vectors of observed mixture signals. We show that these 1-D subspaces are associated with TF points where only single Source possesses dominant energy. By grouping the vectors in these subspaces via hierarchical clustering algorithm, we obtain the estimation of the mixing matrix. Finally, the Source signals could be recovered by solving a series of least squares problems. Since the sparse coding strategy considers the linear representation relations among all the TF representation vectors of mixing signals, the proposed algorithm can provide an accurate estimation of the mixing matrix and is robust to the noises compared with the existing underdetermined Blind Source Separation approaches. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method.
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underdetermined Blind Source Separation by parallel factor analysis in time frequency domain
Cognitive Computation, 2013Co-Authors: Liu Yang, Yong XiangAbstract:This paper presents a new time-frequency approach to the underdetermined Blind Source Separation using the parallel factor decomposition of third-order tensors. Without any constraint on the number of active Sources at an auto-term time-frequency point, this approach can directly separate the Sources as long as the uniqueness condition of parallel factor decomposition is satisfied. Compared with the existing two-stage methods where the mixing matrix should be estimated at first and then used to recover the Sources, our approach yields better Source Separation performance in the presence of noise. Moreover, the mixing matrix can be estimated at the same time of the Source Separation process. Numerical simulations are presented to show the superior performance of the proposed approach to some of the existing two-stage Blind Source Separation methods that use the time-frequency representation as well.
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time frequency approach to underdetermined Blind Source Separation
IEEE Transactions on Neural Networks, 2012Co-Authors: Shengli Xie, Guoxu Zhou, Liu Yang, Junmei Yang, Yong XiangAbstract:This paper presents a new time-frequency (TF) underdetermined Blind Source Separation approach based on Wigner-Ville distribution (WVD) and Khatri-Rao product to separate N non-stationary Sources from M(M <; N) mixtures. First, an improved method is proposed for estimating the mixing matrix, where the negative value of the auto WVD of the Sources is fully considered. Then after extracting all the auto-term TF points, the auto WVD value of the Sources at every auto-term TF point can be found out exactly with the proposed approach no matter how many active Sources there are as long as N ≤ 2M-1. Further discussion about the extraction of auto-term TF points is made and finally the numerical simulation results are presented to show the superiority of the proposed algorithm by comparing it with the existing ones.
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Blind Source Separation using second order cyclostationary statistics
IEEE Transactions on Signal Processing, 2001Co-Authors: Karim Abedmeraim, Jonathan H Manton, Yong Xiang, Yingbo HuaAbstract:This paper studies the Blind Source Separation (BSS) problem with the assumption that the Source signals are cyclostationary. Identifiability and separability criteria based on second-order cyclostationary statistics (SOCS) alone are derived. The identifiability condition is used to define an appropriate contrast function. An iterative algorithm (ATH2) is derived to minimize this contrast function. This algorithm separates the Sources even when they do not have distinct cycle frequencies.
Te-won Lee - One of the best experts on this subject based on the ideXlab platform.
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Fast fixed-point independent vector analysis algorithms for convolutive Blind Source Separation
Signal Processing, 2007Co-Authors: Intae Lee, Taesu Kim, Te-won LeeAbstract:A new type of independent component analysis (ICA) model showed excellence in tackling the Blind Source Separation problem in the frequency domain. The new model, called independent vector analysis, is an extension of ICA for (independent) multivariate Sources where the Sources are mixed component-wise. In this work we examine available contrasts for the new formulation that can solve the frequency-domain Blind Source Separation problem. Also, we introduce a quadratic Taylor polynomial in the notations of complex variables which is very useful in directly applying Newton's method to a contrast function of complex-valued variables. The use of the form makes the derivation of a Newton update rule simple and clear. Fast fixed-point Blind Source Separation algorithms are derived and the performance is shown by experimental results.
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Blind Source Separation in mobile environments using a priori knowledge
International Conference on Acoustics Speech and Signal Processing, 2004Co-Authors: Erik Visser, Te-won LeeAbstract:A speech enhancement scheme including Blind Source Separation and background denoising based on minimum statistics is studied in mobile environments. To accommodate the dependence of the separated output signals on the spatial properties of the recorded Source signals, these Blind signal processing steps are complemented by an adaptive separated output channel selection stage using prior knowledge about the desired speaker speech content. The resulting scheme performance is illustrated by speech recognition experiments on real recordings corrupted by various noise Sources and shown to outperform conventional beamforming and single channel denoising techniques as well as an equivalent scheme with fixed output channel selection.
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Blind Source Separation of more Sources than mixtures using overcomplete representations
IEEE Signal Processing Letters, 1999Co-Authors: Te-won Lee, Michael S. Lewicki, Mark Girolami, Terrence J. SejnowskiAbstract:Empirical results were obtained for the Blind Source Separation of more Sources than mixtures using a previously proposed framework for learning overcomplete representations. This technique assumes a linear mixing model with additive noise and involves two steps: (1) learning an overcomplete representation for the observed data and (2) inferring Sources given a sparse prior on the coefficients. We demonstrate that three speech signals can be separated with good fidelity given only two mixtures of the three signals. Similar results were obtained with mixtures of two speech signals and one music signal.
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Blind Source Separation of nonlinear mixing models
Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop, 2026Co-Authors: Te-won Lee, Bert-uwe Koehler, Reinhold OrglmeisterAbstract:We present a new set of learning rules for the nonlinear Blind Source Separation problem based on the information maximization criterion. The mixing model is divided into a linear mixing part and a nonlinear transfer channel. The proposed model focuses on a parametric sigmoidal nonlinearity and higher order polynomials. Our simulation results verify the convergence of the proposed algorithms.
Ing Yann Soon - One of the best experts on this subject based on the ideXlab platform.
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partial Separation method for solving permutation problem in frequency domain Blind Source Separation of speech signals
Neurocomputing, 2008Co-Authors: V G Reju, Soo Ngee Koh, Ing Yann SoonAbstract:This paper addresses the well known permutation problem in frequency domain Blind Source Separation. The proposed method uses correlation between two signals in each DFT bin to solve the permutation problem. One of the signals is partially separated by a time domain Blind Source Separation method and the other is obtained by the frequency domain Blind Source Separation method. Two different ways of configuring the time and frequency domain blocks, i.e., in parallel or cascade, have been studied. The cascaded configuration not only achieves a better Separation performance but also reduces the computational cost as compared to the parallel configuration.