Abstract
In this paper, a novel blind separation approach using power spectral density(PSD) is presented. The power spectrum itself is the Fourier transform of the auto-correlation function. Auto-correlation function represents the relationship of long and short-term correlation within the signal itself. This paper using power spectral density and cross power spectral density separate blind mixed source signals. In practice, non-stationary signals always have different PSD. The method is suitable for dealing with non-stationary signal. And simulation results have shown that the method is feasible.
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Wang, J., Zhao, Y. (2011). Blind Source Separation Based on Power Spectral Density. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_77
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DOI: https://doi.org/10.1007/978-3-642-23887-1_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23886-4
Online ISBN: 978-3-642-23887-1
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