Abstract
We address the blind source separation (BSS) problem for the convolutive mixing case. Second-order statistical methods are employed assuming the source signals are non-stationary and possibly also non-white. The proposed algorithm is based on a joint-diagonalization approach, where we search for a single polynomial matrix that jointly diagonalizes a set of measured spatiotemporal correlation matrices. In contrast to most other algorithms based on similar concepts, we define the underlying cost function entirely in the time-domain. Furthermore, we present an efficient implementation of the proposed algorithm which is based on fast convolution techniques.
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© 2004 Springer-Verlag Berlin Heidelberg
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Joho, M. (2004). Blind Signal Separation of Convolutive Mixtures: A Time-Domain Joint-Diagonalization Approach. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_74
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DOI: https://doi.org/10.1007/978-3-540-30110-3_74
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