Proceedings Article10.1109/ICASSP.2006.1661407
Eigenvector Algorithms Using Reference Signals
Mitsuru Kawamoto,K. Kohno,Yujiro Inouye +2 more
- 14 May 2006
- Vol. 5, pp 841-844
TL;DR: This paper presents an eigenvector algorithm (EVA) derived from a criterion using reference signals, in which the EVA is applied to the blind source separation (BSS) of instantaneous mixtures.
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Abstract: This paper presents an eigenvector algorithm (EVA) derived from a criterion using reference signals, in which the EVA is applied to the blind source separation (BSS) of instantaneous mixtures. The proposed EVA works such that source signals are simultaneously separated from their mixtures. This is a new result, which has not been clarified by the conventional researches. Simulation results show the validity of the proposed EVA.
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Citations
Eigenvector Algorithms Incorporated With Reference Systems for Solving Blind Deconvolution of MIMO-IIR Linear Systems
TL;DR: This letter presents an eigenvector algorithm (EVA) for blind deconvolution (BD) of multiple-input multiple-output infinite impulse response (MIMO-IIR) channels (convolutive mixtures), using the idea of reference signals.
18
Generalized Identifiability Conditions for Blind Convolutive MIMO Separation
Marc Castella,Eric Moreau +1 more
TL;DR: This correspondence deals with the problem of source separation in the case where the output of a multivariate convolutive mixture is observed: novel and generalized conditions for the blind identifiability of a separating system are proposed.
8
Eigenvector Algorithms for Blind Deconvolution of MIMO-IIR Systems
Mitsuru Kawamoto
- 27 May 2007
TL;DR: This paper presents eigenvector algorithms (EVAs) for blind deconvolution (BD) of multiple-input multiple-output infinite impulse response (MIMO-IIR) channels (convolutive mixtures) using the idea of reference signals, and one of the novel points of the paper is that the EVA using any reference signal is applied to the BD problem of the MIMO.
Robust eigenvector algorithms for blind deconvolution of mimo linear systems
TL;DR: Simulation results show the validity of the proposed eigenvector algorithms for blind deconvolution of multiple-input, multiple-output infinite impulse response channels (convolutive mixtures).
An eigenvector algorithm with reference signals using a deflation approach for blind deconvolution
Mitsuru Kawamoto,Yujiro Inouye,K. Kohno,Takehide Maeda +3 more
- 09 Sep 2007
TL;DR: An eigenvector algorithm with reference signals for blind deconvolution (BD) of multiple-input multiple-output infinite impulse response (MIMO-IIR) channels that can be achieved without using whitening techniques is proposed.
References
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Authors' Reply to Comments on 'New criteria for blind deconvolution of nonminimum phase systems (channels)'.
Ofir Shalvi,E. Weinstein +1 more
TL;DR: A necessary and sufficient condition for blind deconvolution (without observing the input) of nonminimum-phase linear time-invariant systems (channels) is derived.
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Super-exponential methods for blind deconvolution
Ofir Shalvi,E. Weinstein +1 more
TL;DR: A class of iterative methods for solving the blind deconvolution problem, i.e. for recovering the input of an unknown possibly nonminimum-phase linear system by observation of its output, is presented and shows that in many cases of practical interest the performance of the proposed methods is far superior to linear prediction methods even for minimum phase systems.
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Super-exponential methods for blind deconvolution
Ofir Shalvi,E. Weinstein +1 more
TL;DR: A class of iterative methods for solving the problem of blind deconvolution of an unknown possibly non-minimum phase linear system driven by an unobserved input process that converge monotonically at a very fast super-exponential rate to the desired solution.
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