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Daniël W.e. Schobben - One of the best experts on this subject based on the ideXlab platform.
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A frequency domain Blind Signal Separation method based on decorrelation
IEEE Transactions on Signal Processing, 2002Co-Authors: Daniël W.e. Schobben, Piet C W Pcw C W Piet C W Pcw SommenAbstract:This paper addresses the issue of separating multiple speakers from mixtures of these that are obtained using multiple microphones in a room. An adaptive Blind Signal Separation algorithm, which is entirely based on second-order statistics, is derived. One of the advantages of this algorithm is that no parameters need to be tuned. Moreover, an extension of the algorithm that can simultaneously deal with Blind Signal Separation and echo cancellation is derived. Experiments with real recordings have been carried out, showing the effectiveness of the algorithm for real-world Signals
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Real-Time Adaptive Concepts in Acoustics: Blind Signal Separation and Multichannel Echo Cancellation
2001Co-Authors: Daniël W.e. SchobbenAbstract:List of Figures. Preface. Part I: Background and Introduction. 1. Introduction. 2. Array Processing Techniques. 3. Efficient Filtering Using FFTS. Part II: Acoustic Echo Cancellation. 4. An Efficient Adaptive Filter Implementation. 5. Efficient Multichannel RLS. Part III: Blind Signal Separation. 6. Blind Signal Separation, An Overview. 7. A Blind Signal Separation Algorithm. 8. Joint Blind Signal Separation and Echo Cancellation. 9. Blind Signal Separation Algorithm Evaluation. 10. Conclusions. Appendices.
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A Blind Signal Separation Algorithm
Real-time Adaptive Concepts in Acoustics, 2001Co-Authors: Daniël W.e. SchobbenAbstract:This chapter addresses the problem of separating multiple speakers from mixtures of these that are obtained using multiple microphones in a room. A new Blind Signal Separation algorithm is derived which is entirely based on second order statistics. The algorithm can run in off-line or online (adaptive) mode. One of the advantages of this algorithm is that no assumptions are made about the probability density functions or other properties of the Signals. The Blind Signal Separation algorithm has been tested using microphones that pickup speech Signals of speakers that are talking simultaneously to give an indication of its performance for real-world data.
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Joint Blind Signal Separation And Echo Cancellation
Real-time Adaptive Concepts in Acoustics, 2001Co-Authors: Daniël W.e. SchobbenAbstract:The problem of joint Blind Signal Separation and acoustic echo cancellation arises in applications such as teleconferencing and voice-controlled machinery. Typically, in the same room as the local speakers there are loudspeakers reproducing far-end speech or music Signals. The contributions of these loudspeaker Signals to the microphone Signals need to be canceled. The remaining Signals are then separated so that the individual local speakers are recovered. In the previous chapter the CoBliSS Blind Signal Separation algorithm is introduced. In this chapter an extension of CoBliSS is presented (ECoBliSS) which can simultaneously deal with Blind Signal Separation and echo cancellation. The computational complexity of ECoBliSS is less than that of CoBliSS when processing the same number of Signals. The performance of the extended CoBliSS algorithm is evaluated using audio recorded in a real acoustic environment. This chapter is based on Schobben and Sommen, 1999a.
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Blind Signal Separation, An Overview
Real-time Adaptive Concepts in Acoustics, 2001Co-Authors: Daniël W.e. SchobbenAbstract:Blind Signal Separation (BSS) is the process of recovering independent Signals that correspond to the individual source Signals using only observed linear mixtures of these. In an acoustic context, these source Signals are correlated in time and are assumed to be independent of each other. The mixing system is convolutive in the sense that the microphones pick up delayed and attenuated versions of the source Signals due to reflections in the room. The microphone Signals typically contain some microphone noise.
Piet C W Pcw C W Piet C W Pcw Sommen - One of the best experts on this subject based on the ideXlab platform.
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A frequency domain Blind Signal Separation method based on decorrelation
IEEE Transactions on Signal Processing, 2002Co-Authors: Daniël W.e. Schobben, Piet C W Pcw C W Piet C W Pcw SommenAbstract:This paper addresses the issue of separating multiple speakers from mixtures of these that are obtained using multiple microphones in a room. An adaptive Blind Signal Separation algorithm, which is entirely based on second-order statistics, is derived. One of the advantages of this algorithm is that no parameters need to be tuned. Moreover, an extension of the algorithm that can simultaneously deal with Blind Signal Separation and echo cancellation is derived. Experiments with real recordings have been carried out, showing the effectiveness of the algorithm for real-world Signals
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WDM monitoring technique using adaptive Blind Signal Separation
IEEE Photonics Technology Letters, 2001Co-Authors: Eduward Tangdiongga, Piet C W Pcw C W Piet C W Pcw Sommen, Nicola Calabretta, H.j.s. DorrenAbstract:We present a cost-effective method to monitor the performance of wavelength-division-multiplexed (WDM) channels. The method is based on simple optical Signal processing in a combination with electronic Signal processing. The photocurrent of a detected (multichannel) optical Signal is analyzed using an adaptive Blind Signal Separation method. A maximum data decorrelation criterion is used to separate the WDM channels. We show experimentally that four WDM channels can be reconstructed accurately by this numerical method.
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Realtime realization aspects of the CoBliSS Blind Signal Separation algorithm
1998Co-Authors: Daniël W.e. Schobben, Piet C W Pcw C W Piet C W Pcw SommenAbstract:Recently, the Convolutive Blind Signal Separation algorithm (CoBliSS) was introduced. CoBliSS is based on second order statics only and is able to control a multichannel filter with thousands of tabs as is required in acoustical applications. In this paper the feasibility of a real-time implementation of the CoBliSS algorithm is investigated. An efficient implementation is proposed and the corresponding computational complexity is discussed.
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ISSPA - A new algorithm for joint Blind Signal Separation and acoustic echo canceling
ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359), 1Co-Authors: D.w.e. Schobben, Piet C W Pcw C W Piet C W Pcw SommenAbstract:The problem of joint Blind Signal Separation and acoustic echo cancelling arises in applications such as teleconferencing and voice controlled machinery. Microphones pick up a Signal of the desired speaker together with contributions of other speakers and loudspeakers in these applications. The contributions of these loudspeaker Signals to the microphone Signals need to be cancelled. The remaining Signals are then separated so that the individual local speakers are recovered. In this paper an extension of the previously introduced convolutive Blind Signal Separation algorithm, CoBliSS is presented. This extended algorithm is capable of performing combined Blind Signal Separation and acoustical echo cancelling at a low computational cost. The performance of the extended CoBliSS algorithm is evaluated using audio that is recorded in a real acoustical environment.
Xie-ting Ling - One of the best experts on this subject based on the ideXlab platform.
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FECG detection based on Blind Signal Separation
Chinese Journal of Biomedical Engineering, 2002Co-Authors: Xin Li, Xie-ting Ling, Zhi Liu, Bo Hu, Cai ChangAbstract:A new method of Blind Signal Separation was provided to achieve FECG (Fetal ECG) detection. The lead-system characteristic for pregnant ECG Signal was studied at first, then an on-line FECG monitoring system was designed. Because the on-line learning algorithm of Blind Signal Separation was used, FECG Signal can be detected in real-time. Some experimental results proved this monitoring system to be an efficient one.
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ICASSP - Blind Signal Separation for MA mixture model
1995 International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Xie-ting Ling, Wei Tian, Bin LiuAbstract:The paper presents a linear feedback neural network and an adaptive algorithm to achieve the Blind Signal Separation under near-field situation. The convergence property of algorithm and stability of equilibrium state are discussed. Some simulations are provided.
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ISCAS - A neural network for Blind Signal Separation
Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 1Co-Authors: Xie-ting Ling, Y.f. Huang, Ruey-wen LiuAbstract:An unsupervised neural network is constructed for the problem of Blind Signal Separation. It is designed based on the condition that the outputs of the neural network are independent. A study of the stability of the neural network in the sense of expectation is presented. A stability condition on the system matrix A is obtained. Simulation studies show that this neural network is robust. It can separate two Signals with strength ratio 100:1. >
Nadege Thirion-moreau - One of the best experts on this subject based on the ideXlab platform.
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Convolutive Blind Signal Separation based on asymmetrical contrast functions
IEEE Transactions on Signal Processing, 2007Co-Authors: Eric Moreau, Jean-christophe Pesquet, Nadege Thirion-moreauAbstract:In this paper, we consider the Blind Signal Separation problem for convolutive mixtures, in the real case. More precisely, we present a generalization of classical contrast functions to more flexible asymmetric forms. We provide several examples of these new criteria which are useful for sources having different high-order statistics. We also perform a statistical study of the proposed source Separation approach, including both the consistency and the asymptotic normality aspects. These theoretical results are also confirmed by numerical simulations
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ICASSP - Performance analysis of Blind Signal Separation methods based on asymmetric contrast functions
IEEE International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Jean-christophe Pesquet, Eric Moreau, Nadege Thirion-moreauAbstract:In this paper, we consider the Blind Signal Separation problem in the convolutive case. More precisely, we present a generalization of classical contrast functions to more flexible asymmetric forms and give examples of these new criteria. We also realize a statistical study of the proposed source Separation approach, including both the consistency and the asymptotic normality aspects.
Scott C. Douglas - One of the best experts on this subject based on the ideXlab platform.
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Overview of Blind Signal Separation, part I: Criteria and algorithms
The Journal of the Acoustical Society of America, 2000Co-Authors: Scott C. DouglasAbstract:In Blind Signal Separation, multiple independent source Signals are separated from multiple linear mixtures of these Signals without specific knowledge of either the source Signal characteristics or the mixing conditions. This talk provides an introduction to the Blind Signal Separation task. Three different problem formulations—Signal Separation of instantaneous mixtures, Signal Separation of convolutive mixtures, and multichannel Blind deconvolution—are described, and their similarities and differences are highlighted. An overview of both information‐theoretic and contrast‐based Separation criteria is then given. Natural gradient optimization procedures, when combined with such criteria, yield simple and useful Blind Signal Separation algorithms. An example of speech Separation of real room recordings illustrates the capabilities and limitations of one such approach.
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ICASSP - On optimal and universal nonlinearities for Blind Signal Separation
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1Co-Authors: Heinz Mathis, Scott C. DouglasAbstract:The search for universally applicable nonlinearities in Blind Signal Separation has produced nonlinearities that are optimal for a given distribution, as well as nonlinearities that are most robust against model mismatch. This paper shows yet another justification for the score function, which is in some sense a very robust nonlinearity. It also shows that among the class of parameterizable nonlinearities, the threshold nonlinearity with the threshold as a parameter is able to separate any non-Gaussian distribution, a fact that is also proven in this paper.