A new method for kurtosis maximization and source separation

Abstract : This paper introduces a new method to maximize kurtosis-based contrast functions. Such contrast functions appear in the problem of blind source separation of convolutively mixed sources: the corresponding methods recover the sources one by one using a deflation approach. The proposed maximization algorithm is based on the particular nature of the criterion. The method is similar in spirit to a gradient ascent method, but differs in the fact that a "reference" contrast function is considered at each line search. The convergence of the method to a stationary point of the criterion can be proved. The theoretical result is illustrated by simulation
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [4 references]  Display  Hide  Download

Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Wednesday, April 27, 2016 - 10:40:21 AM
Last modification on : Friday, April 5, 2019 - 8:07:55 PM
Document(s) archivé(s) le : Thursday, July 28, 2016 - 10:41:43 AM


Files produced by the author(s)



Marc Castella, Eric Moreau. A new method for kurtosis maximization and source separation. ICASSP 2010 : 35th International Conference on Acoustics, Speech, and Signal Processing , Mar 2010, Dallas, United States. pp.2670 - 2673, ⟨10.1109/ICASSP.2010.5496250 ⟩. ⟨hal-01308032⟩



Record views


Files downloads