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192
An Approach to Blind Source Separation Based on Temporal Structure of Speech Signals
- Neurocomputing
, 1998
"... In this paper we introduce a new technique for blind source separation of speech signals. We focus on the temporal structure of the signals in contrast to most other major approaches to this problem. The idea is to apply the decorrelation method proposed by Molgedey and Schuster in the time-frequenc ..."
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Cited by 109 (5 self)
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In this paper we introduce a new technique for blind source separation of speech signals. We focus on the temporal structure of the signals in contrast to most other major approaches to this problem. The idea is to apply the decorrelation method proposed by Molgedey and Schuster in the time-frequency domain. We show some results of experiments with both artificially controlled data and speech data recorded in the real environment.
Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms
, 2004
"... Non-invasive EEG recordings provide for easy and safe access to human neocortical processes which can be exploited for a Brain-Computer Interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. Here, we systematically analyze and furthermore develop two rec ..."
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Cited by 96 (19 self)
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Non-invasive EEG recordings provide for easy and safe access to human neocortical processes which can be exploited for a Brain-Computer Interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. Here, we systematically analyze and furthermore develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: (1) the combination of classifiers each specifically tailored for different physiological phenomena, e.g. slow cortical potential shifts, such as the pre-movement Bereitschaftspotential, or differences in spatiospectral distributions of brain activity (i.e. focal event-related desynchronizations), and (2) behavioral paradigms inducing the subjects to generate one out of several brain states (multi- class approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show in particular that a suitably arranged interaction between these concepts can significantly boost BCI performances.
ICA Using Spacings Estimates of Entropy
- Journal of Machine Learning Research
, 2003
"... This paper presents a new algorithm for the independent components analysis (ICA) problem based on an efficient entropy estimator. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated Kullback-Leibler divergence betwee ..."
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Cited by 74 (3 self)
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This paper presents a new algorithm for the independent components analysis (ICA) problem based on an efficient entropy estimator. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated Kullback-Leibler divergence between the joint distribution and the product of the marginal distributions. We pair this approach with efficient entropy estimators from the statistics literature. In particular, the entropy estimator we use is consistent and exhibits rapid convergence. The algorithm based on this estimator is simple, computationally efficient, intuitively appealing, and outperforms other well known algorithms. In addition, the estimator's relative insensitivity to outliers translates into superior performance by our ICA algorithm on outlier tests. We present favorable comparisons to the Kernel ICA, FAST-ICA, JADE, and extended Infomax algorithms in extensive simulations. We also provide public domain source code for our algorithms.
A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics,”
- IEEE Transactions on Speech and Audio Processing,
, 2005
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Computation of the canonical decomposition by means of a simultaneous generalized schur decomposition
- SIAM J. Matrix Anal. Appl
, 2004
"... Abstract. The canonical decomposition of higher-order tensors is a key tool in multilinear algebra. First we review the state of the art. Then we show that, under certain conditions, the problem can be rephrased as the simultaneous diagonalization, by equivalence or congruence, of a set of matrices. ..."
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Cited by 55 (10 self)
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Abstract. The canonical decomposition of higher-order tensors is a key tool in multilinear algebra. First we review the state of the art. Then we show that, under certain conditions, the problem can be rephrased as the simultaneous diagonalization, by equivalence or congruence, of a set of matrices. Necessary and sufficient conditions for the uniqueness of these simultaneous matrix decompositions are derived. In a next step, the problem can be translated into a simultaneous generalized Schur decomposition, with orthogonal unknowns [A.-J. van der Veen and A. Paulraj, IEEE Trans. Signal Process., 44 (1996), pp. 1136–1155]. A first-order perturbation analysis of the simultaneous generalized Schur decomposition is carried out. We discuss some computational techniques (including a new Jacobi algorithm) and illustrate their behavior by means of a number of numerical experiments.
Independent Components of Magnetoencephalography: Localization
, 2002
"... We applied second-order blind identification (SOBI), an independent component analysis (ICA) method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification ..."
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Cited by 44 (12 self)
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We applied second-order blind identification (SOBI), an independent component analysis (ICA) method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole (ECD) modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations.
Canonical Tensor Decompositions
- ARCC WORKSHOP ON TENSOR DECOMPOSITION
, 2004
"... The Singular Value Decomposition (SVD) may be extended to tensors at least in two very different ways. One is the High-Order SVD (HOSVD), and the other is the Canonical Decomposition (CanD). Only the latter is closely related to the tensor rank. Important basic questions are raised in this short pap ..."
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Cited by 43 (16 self)
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The Singular Value Decomposition (SVD) may be extended to tensors at least in two very different ways. One is the High-Order SVD (HOSVD), and the other is the Canonical Decomposition (CanD). Only the latter is closely related to the tensor rank. Important basic questions are raised in this short paper, such as the maximal achievable rank of a tensor of given dimensions, or the computation of a CanD. Some questions are answered, and it turns out that the answers depend on the choice of the underlying field, and on tensor symmetry structure, which outlines a major difference compared to matrices.
Fourth-order cumulant-based blind identification of underdetermined mixtures
- SIGNAL PROCESSING, IEEE TRANSACTIONS ON
, 2007
"... In this paper we study two fourth-order cumulantbased techniques for the estimation of the mixing matrix in underdetermined independent component analysis. The first method is based on a simultaneous matrix diagonalization. The second is based on a simultaneous off-diagonalization. The number of so ..."
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Cited by 42 (0 self)
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In this paper we study two fourth-order cumulantbased techniques for the estimation of the mixing matrix in underdetermined independent component analysis. The first method is based on a simultaneous matrix diagonalization. The second is based on a simultaneous off-diagonalization. The number of sources that can be allowed is roughly quadratic in the number of observations. For both methods, explicit expressions for the maximum number of sources are given. Simulations illustrate the performance of the techniques.
Algebraic Methods for Deterministic Blind Beamforming
, 1998
"... Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on a phased antenna array by using deterministic properties of the signals or the channels such as their constant modulus or directions-of-arrival. Progress in this area has been abundant over the p ..."
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Cited by 38 (6 self)
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Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on a phased antenna array by using deterministic properties of the signals or the channels such as their constant modulus or directions-of-arrival. Progress in this area has been abundant over the past ten years and has resulted in several powerful algorithms. Unlike optimal or adaptive methods, the algebraic methods discussed in this review act on a fixed block of data and give closed-form expressions for beamformers by focusing on algebraic structures. This typically leads to subspace estimation and generalized eigenvalue problems. After introducing a simple and widely used multipath channel model, the paper provides an anthology of properties that are available, and generic algorithms that exploit them.
