Abstract
In the context of Blind Source Separation (BSS), we consider the problem of online separation of stationary sources. Based on the Maximum Likelihood (ML) solution for semi-blind separation of temporally-diverse Gaussian sources, and assuming that a parametric model of the sources' spectra is available, we propose an online adaptive Quasi-ML (QML) separation algorithm. The algorithm operates in an alternating fashion, updating at each iteration the (nuisance) spectra-characterizing parameters first, and then the demixing-matrix estimates, according to simple, computationally efficient update expressions which we derive. Our proposed algorithm, which leads to consistent separation of the sources, is demonstrated here, both analytically and empirically in a simulation experiment, for first-order autoregressive sources.
Original language | English |
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Title of host publication | 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781538663783 |
DOIs | |
State | Published - 2 Jul 2018 |
Externally published | Yes |
Event | 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 - Eilat, Israel Duration: 12 Dec 2018 → 14 Dec 2018 |
Publication series
Name | 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 |
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Conference
Conference | 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 |
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Country/Territory | Israel |
City | Eilat |
Period | 12/12/18 → 14/12/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Blind source separation
- SeDJoCo
- independent component analysis
- quasi-maximum likelihood