Abstract
The principal aim of this work is to demonstrate, from an empirical point of view, the effectiveness of a previously proposed technique for the Blind Source Separation (BSS) with Post Non Linear (PNL) underdetermined instantaneous mixing model (uBSS), in the more complex and realistic case where delayed sources are considered in the linear part of the mixing (PNL-uBSS with delays). The proposed approach is composed of two consecutive stages: in the first stage the inverse nonlinearities are estimated by Gaussianization of the mixtures; in the second stage source signals are extracted from the linearized mixtures using a three step approach already known in the literature for linear delayed uBSS. An improved technique based on Extended Gaussianization is also provided for the estimation of inverse nonlinearities. Experimental results using synthetic mixtures of real world data (speech signals) are given to prove the effectiveness of the proposed approach.
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Bastari, A., Squartini, S., Cecchi, S., Piazza, F. (2007). Gaussianization Based Approach for Post-Nonlinear Underdetermined BSS with Delays. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_96
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DOI: https://doi.org/10.1007/978-3-540-72395-0_96
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