Abstract
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.
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Saylani, H., Hosseini, S., Deville, Y. (2009). Blind Separation of Noisy Mixtures of Non-stationary Sources Using Spectral Decorrelation. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_41
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DOI: https://doi.org/10.1007/978-3-642-00599-2_41
Publisher Name: Springer, Berlin, Heidelberg
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