Abstract
A new approach for the blind separation of convolutive mixtures is proposed based on sources nonstationarity and the joint diagonalization of the output power spectral density matrices. It utilizes a time-domain separation network, but the coefficients are optimized based on a frequency domain objective function. The proposed algorithm has the advantages associated with frequency domain BSS algorithms for long mixing channels, but does not suffer from permutation ambiguity problem.
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Mei, T., Xi, J., Yin, F., Chicharo, J.F. (2005). Joint Diagonalization of Power Spectral Density Matrices for Blind Source Separation of Convolutive Mixtures. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_85
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DOI: https://doi.org/10.1007/11427445_85
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