Abstract
We consider the problem of joint blind source separation of multiple datasets and introduce an effective solution to the problem. We pose the problem in an independent vector analysis (IVA) framework utilizing the multivariate Gaussian source vector distribution. We provide a new general IVA implementation using a decoupled nonorthogonal optimization algorithm and establish the connection between the new approach and another approach using second-order statistics, multiset canonical correlation analysis. Experimental results are given to demonstrate the success of the new algorithm in achieving reliable source separation for both Gaussian and non-Gaussian sources.
This work is supported by the NSF grants NSF-CCF 0635129 and NSF-IIS 0612076.
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Anderson, M., Li, XL., Adalı, T. (2010). Nonorthogonal Independent Vector Analysis Using Multivariate Gaussian Model. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_44
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DOI: https://doi.org/10.1007/978-3-642-15995-4_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15994-7
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