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Conference Papers Year : 2007

Complex nonconvex lp norm minimization for underdetermined source separation

Abstract

Underdetermined source separation methods often rely on the assumption that the time-frequency source coefficients are independent and Laplacian distributed. In this article, we extend these methods by assuming that these coefficients follow a generalized Gaussian prior with shape parameter p. We study mathematical and experimental properties of the resulting complex nonconvex lp norm optimization problem in a particular case and derive an efficient global optimization algorithm. We show that the best separation performance for three-source stereo convolutive speech mixtures is achieved for small p.
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Dates and versions

inria-00544203 , version 1 (07-12-2010)

Identifiers

  • HAL Id : inria-00544203 , version 1

Cite

Emmanuel Vincent. Complex nonconvex lp norm minimization for underdetermined source separation. 7th Int. Conf. on Independent Component Analysis and Blind Source Separation (ICA), Sep 2007, London, United Kingdom. pp.430--437. ⟨inria-00544203⟩
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