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A blind source separation technique using second-order statistics
Abstract
Separation of sources consists of 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 processing applications when the array manifold is unknown or distorted. This paper introduces a new source separation technique exploiting the time coherence of the source signals. In contrast with other previously reported techniques, the proposed approach relies only on stationary second-order statistics that are based on a joint diagonalization of a set of covariance matrices. Asymptotic performance analysis of this method is carried out; some numerical simulations are provided to illustrate the effectiveness of the proposed method.
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Self-citationBlind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture
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Blind separation of mixture of independent sources through a quasi-maximum likelihood approach
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AMUSE: a new blind identification algorithm
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Deep learning in neural networks: an overview
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Single-trial analysis and classification of ERP components — A tutorial
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A unifying information-theoretic framework for independent component analysis
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Knowledge graph completion via complex tensor factorization
(2017) · arXiv (Cornell University) · doi:10.5555/3122009.3208011 · Cited by 211
Metadata
- Authors
- Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., & Moulines, E.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1997
- Volume
- 45
- Issue
- 2
- Pages
- 434-444
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2142638745
- Cited by
- 2,775
- References
- 9
- FWCI
- 40.17
- Type
- journal article
- Language
- en
- Open access
- closed
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