Abstract
When dealing with nonlinear blind deconvolution, complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing or in microarray data analysis. In this paper we propose a simple method to reduce computational time for the inversion of Wiener systems by using a linear approximation in a minimum-mutual information algorithm. Experimental results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased.
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Solé-Casals, J., Caiafa, C.F. (2011). A Simple Approximation for Fast Nonlinear Deconvolution. In: Travieso-González, C.M., Alonso-Hernández, J.B. (eds) Advances in Nonlinear Speech Processing. NOLISP 2011. Lecture Notes in Computer Science(), vol 7015. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25020-0_8
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DOI: https://doi.org/10.1007/978-3-642-25020-0_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25019-4
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