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Blind Signal Separation: Statistical Principles
, 2003
"... Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mut ..."
Abstract

Cited by 522 (4 self)
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Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption
A new learning algorithm for blind signal separation

, 1996
"... A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of ..."
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Cited by 614 (80 self)
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A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number
A Fast and Efficient FrequencyDomain Method for Convolutive Blind Source Separation *
"... AbstractIn this paper, the problem of blind separation of a convolutive mixture of audio signals is considered. A fast and efficient frequencydomain Blind Source Separation (BSS) method using Independent Component Analysis (ICA) is investigated. The main difficulties of this approach lie in the so ..."
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AbstractIn this paper, the problem of blind separation of a convolutive mixture of audio signals is considered. A fast and efficient frequencydomain Blind Source Separation (BSS) method using Independent Component Analysis (ICA) is investigated. The main difficulties of this approach lie
A robust and precise method for solving the permutation problem of frequencydomain blind source separation
 IEEE Trans. on Speech and Audio Processing 12
, 2004
"... This paper presents a robust and precise method for solving the permutation problem of frequencydomain blind source separation. It is based on two previous approaches: the direction of arrival estimation and the interfrequency correlation. We discuss the advantages and disadvantages of the two app ..."
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Cited by 115 (27 self)
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This paper presents a robust and precise method for solving the permutation problem of frequencydomain blind source separation. It is based on two previous approaches: the direction of arrival estimation and the interfrequency correlation. We discuss the advantages and disadvantages of the two
Spectral smoothing for frequencydomain blind source separation
 in Proc. IWAENC 2003
"... This paper describes the circularity problem of frequencydomain blind source separation (BSS), and presents a new method for solving it. Frequencydomain BSS performs independent component analysis (ICA) in each frequency bin. It is more efficient than timedomain BSS where ICA is applied to convolu ..."
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Cited by 5 (3 self)
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This paper describes the circularity problem of frequencydomain blind source separation (BSS), and presents a new method for solving it. Frequencydomain BSS performs independent component analysis (ICA) in each frequency bin. It is more efficient than timedomain BSS where ICA is applied
1A Robust and Precise Method for Solving the Permutation Problem of FrequencyDomain Blind Source Separation
"... Abstract — Blind source separation (BSS) for convolutive mixtures can be solved efficiently in the frequency domain, where independent component analysis (ICA) is performed separately in each frequency bin. However, frequencydomain BSS involves a permutation problem: the permutation ambiguity of I ..."
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Abstract — Blind source separation (BSS) for convolutive mixtures can be solved efficiently in the frequency domain, where independent component analysis (ICA) is performed separately in each frequency bin. However, frequencydomain BSS involves a permutation problem: the permutation ambiguity
Solving the Permutation and Circularity Problems of FrequencyDomain Blind Source Separation
"... Blind source separation (BSS) for convolutive mixtures can be performed efficiently in the frequency domain, where independent component analysis (ICA) is applied separately in each frequency bin. However, frequencydomain BSS involves two major problems that must be solved. The first is the permuta ..."
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Blind source separation (BSS) for convolutive mixtures can be performed efficiently in the frequency domain, where independent component analysis (ICA) is applied separately in each frequency bin. However, frequencydomain BSS involves two major problems that must be solved. The first
A robust approach to the permutation problem of frequencydomain blind source separation
 in Proc. ICASSP
, 2003
"... This paper presents a robust and precise method for solving the permutation problem of frequencydomain blind source separation. It is based on two previous approaches: the direction of arrival estimation approach and the interfrequency correlation approach. We discuss the advantages and disadvanta ..."
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Cited by 10 (6 self)
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This paper presents a robust and precise method for solving the permutation problem of frequencydomain blind source separation. It is based on two previous approaches: the direction of arrival estimation approach and the interfrequency correlation approach. We discuss the advantages
Blind Beamforming for Non Gaussian Signals
 IEE ProceedingsF
, 1993
"... This paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resorting to their hypothesized value. By using estimates of the directional vectors obtained via blind identification i.e. without knowing the arrray mani ..."
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Cited by 704 (31 self)
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range of parameters, even when the array is perfectly known to the informed beamformer. The key assumption blind identification relies on is the statistical independence of the sources, which we exploit using fourthorder cumulants. A computationally efficient technique is presented for the blind
Convolutive Blind Separation of NonStationary
"... Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle the probl ..."
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Cited by 193 (3 self)
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Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle
Results 1  10
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