In this paper, we propose a new approach for blind separation of noisy, over-determined, linear instantaneous mixtures of non- stationary sources. This approach is an extension of a new method based on spectral decorrelation that we have recently proposed. Contrary to classical second-order blind source separation (BSS) algorithms, our proposed approach only requires the non-stationary sources and the stationary noise signals to be instantaneously mutually uncorrelated. Thanks to this assumption, it works even if the noise signals are auto-correlated. The simulation results show the much better performance of our approach in comparison to some classical BSS algorithms. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Saylani, H., Hosseini, S., & Deville, Y. (2009). Blind separation of noisy mixtures of non-stationary sources using spectral decorrelation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5441, pp. 322–329). https://doi.org/10.1007/978-3-642-00599-2_41
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