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2,833
Structuring time domain blind source separation algorithms for casa integration
 In Proc. ISCA Tutorial and Research Workshop on Statistical and Perceptual Audition (SAPA
, 2006
"... Most algorithms based on Computational Auditory Scene Analysis (CASA) for binaural speech separation do not have the ability to inhibit already localized and for a long time present sources in the auditory scene. This has the major drawback that the auditory cues of weaker and new sources are subjec ..."
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Cited by 1 (0 self)
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domain blind source separation algorithm which was derived from a statistical signal processing viewpoint and exhibits good convergence even in reverberant environments. Finally, we discuss how the insights gained from building a blind source separation this way can be used to integrate CASA techniques
OnLine TimeDomain Blind Source Separation of Nonstationary Convolved Signals
, 2003
"... In this paper we propose a timedomain gradient algorithm that exploits the nonstationarity of observed signals and recovers the original sources by simultaneously decorrelating timevarying secondorder statistics. By introducing a generalized weighting factor in our cost function we can formulate a ..."
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Cited by 24 (7 self)
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In this paper we propose a timedomain gradient algorithm that exploits the nonstationarity of observed signals and recovers the original sources by simultaneously decorrelating timevarying secondorder statistics. By introducing a generalized weighting factor in our cost function we can formulate
On the causality problem in timedomain blind source separation and deconvolution algorithms
 in Proc. IEEE ICASSP
, 2005
"... Based on a recently presented generic framework for multichannel blind signal processing for convolutive mixtures we investigate in this paper the problem of incorporating acausal delays which are necessary with certain geometric constellations. Starting from a generic update equation which is appli ..."
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Cited by 7 (6 self)
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is applicable to blind source separation (BSS), multichannel blind deconvolution (MCBD), and multichannel blind partial deconvolution (MCBPD) for dereverberation of speech signals, two formulations of the natural gradient are derived. It is shown that one expression is applicable to mere causal filters whereas
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 ..."
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Cited by 529 (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 622 (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
Enhancement of Noisy Speech Recordings via Blind Source Separation
"... We propose an improved timedomain Blind Source Separation method and apply it to speech signal enhancement using multiple microphone recordings. The improvement consists in utilization of fuzzy clustering instead of a hard one, which is verified by experiments where realworld mixtures of two aud ..."
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We propose an improved timedomain Blind Source Separation method and apply it to speech signal enhancement using multiple microphone recordings. The improvement consists in utilization of fuzzy clustering instead of a hard one, which is verified by experiments where realworld mixtures of two au
A TIME DOMAIN ALGORITHM FOR BLIND SEPARATION OF CONVOLUTIVE SOUND MIXTURES AND L1 CONSTRAINED MINIMIZATION OF CROSS CORRELATIONS∗
"... Abstract. A time domain blind source separation algorithm of convolutive sound mixtures is studied based on a compact partial inversion formula in closed form. An l1constrained minimization problem is formulated to find demixing filter coefficients for source separation while capturing scaling inva ..."
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Cited by 3 (3 self)
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Abstract. A time domain blind source separation algorithm of convolutive sound mixtures is studied based on a compact partial inversion formula in closed form. An l1constrained minimization problem is formulated to find demixing filter coefficients for source separation while capturing scaling
A Blind Source Separation Technique Using Second Order Statistics
, 1997
"... Separation of sources consists in recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: the linear mixture should be `blindly' processed. This typically occurs in narrowband array pro ..."
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Cited by 336 (9 self)
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Separation of sources consists in recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: the linear mixture should be `blindly' processed. This typically occurs in narrowband array
Blind separation of speech mixtures via timefrequency masking
 IEEE TRANSACTIONS ON SIGNAL PROCESSING (2002) SUBMITTED
, 2004
"... Binary timefrequency masks are powerful tools for the separation of sources from a single mixture. Perfect demixing via binary timefrequency masks is possible provided the timefrequency representations of the sources do not overlap: a condition we calldisjoint orthogonality. We introduce here t ..."
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Cited by 322 (5 self)
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Binary timefrequency masks are powerful tools for the separation of sources from a single mixture. Perfect demixing via binary timefrequency masks is possible provided the timefrequency representations of the sources do not overlap: a condition we calldisjoint orthogonality. We introduce here
Underdetermined Blind Source Separation Using Sparse Representations
, 2001
"... The scope of this work is the separation of N sources from M linear mixtures when the underlying system is underdetermined, that is, when M<N. If the input distribution is sparse the mixing matrix can be estimated either by external optimization or by clustering and, given the mixing matrix, a mi ..."
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Cited by 167 (5 self)
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that enclose it. Several experiments with music and speech signals show that their timedomain representation is not sparse enough. Yet, excellent results were obtained using their shorttime Fourier transform, including the separation of up to six sources from two mixtures.
Results 1  10
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2,833