Blind Separation of Independent Sources from Convolutive Mixtures

Pierre COMON; Ludwig ROTA

Blind Separation of Independent Sources from Convolutive Mixtures

Pierre COMON, Ludwig ROTA

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The problem of separating blindly independent sources from a convolutive mixture cannot be addressed in its widest generality without resorting to statistics of order higher than two. The core of the problem is in fact to identify the paraunitary part of the mixture, which is addressed in this paper. With this goal, a family of statistical contrast is first defined. Then it is shown that the problem reduces to a Partial Approximate Joint Diagonalization (PAJOD) of several cumulant matrices. Then, a numerical algorithm is devised, which works block-wise, and sweeps all the output pairs. Computer simulations show the good behavior of the algorithm in terms of Symbol Error Rates, even on very short data blocks.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.3 pp.542-549

Publication Date: 2003/03/01

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Type of Manuscript: Special Section INVITED PAPER (Special Section on Blind Signal Processing: Independent Component Analysis and Signal Separation)

Category: Convolutive Systems

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Pierre COMON, Ludwig ROTA, "Blind Separation of Independent Sources from Convolutive Mixtures" in IEICE TRANSACTIONS on Fundamentals, vol. E86-A, no. 3, pp. 542-549, March 2003, doi: .
Abstract: The problem of separating blindly independent sources from a convolutive mixture cannot be addressed in its widest generality without resorting to statistics of order higher than two. The core of the problem is in fact to identify the paraunitary part of the mixture, which is addressed in this paper. With this goal, a family of statistical contrast is first defined. Then it is shown that the problem reduces to a Partial Approximate Joint Diagonalization (PAJOD) of several cumulant matrices. Then, a numerical algorithm is devised, which works block-wise, and sweeps all the output pairs. Computer simulations show the good behavior of the algorithm in terms of Symbol Error Rates, even on very short data blocks.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_3_542/_p

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@ARTICLE{e86-a_3_542,
author={Pierre COMON, Ludwig ROTA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Blind Separation of Independent Sources from Convolutive Mixtures},
year={2003},
volume={E86-A},
number={3},
pages={542-549},
abstract={The problem of separating blindly independent sources from a convolutive mixture cannot be addressed in its widest generality without resorting to statistics of order higher than two. The core of the problem is in fact to identify the paraunitary part of the mixture, which is addressed in this paper. With this goal, a family of statistical contrast is first defined. Then it is shown that the problem reduces to a Partial Approximate Joint Diagonalization (PAJOD) of several cumulant matrices. Then, a numerical algorithm is devised, which works block-wise, and sweeps all the output pairs. Computer simulations show the good behavior of the algorithm in terms of Symbol Error Rates, even on very short data blocks.},
keywords={},
doi={},
ISSN={},
month={March},}

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TY - JOUR
TI - Blind Separation of Independent Sources from Convolutive Mixtures
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 542
EP - 549
AU - Pierre COMON
AU - Ludwig ROTA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2003
AB - The problem of separating blindly independent sources from a convolutive mixture cannot be addressed in its widest generality without resorting to statistics of order higher than two. The core of the problem is in fact to identify the paraunitary part of the mixture, which is addressed in this paper. With this goal, a family of statistical contrast is first defined. Then it is shown that the problem reduces to a Partial Approximate Joint Diagonalization (PAJOD) of several cumulant matrices. Then, a numerical algorithm is devised, which works block-wise, and sweeps all the output pairs. Computer simulations show the good behavior of the algorithm in terms of Symbol Error Rates, even on very short data blocks.
ER -