Underdetermined blind source separation of convolutive mixtures by hierarchical clustering and L1-norm minimization

Article in Edited Volumes
(Book chapter)


Publication Details

Author(s): Winter S, Kellermann W, Sawada H, Makino S
Editor(s): S. Makino, Te-Won Lee, H. Sawada
Title edited volumes: Blind Speech Separation
Publisher: Springer
Publishing place: Dordrecht
Publication year: 2007
Title of series: Signals and Communication Technology
Pages range: 271-304
ISBN: 978-1-4020-6479-1
Language: English


Abstract

In this chapter we present a complete solution for underdetermined blind
source separation (BSS) of convolutive speech mixtures based on two
stages. In the first stage, the mixing system is estimated, for which we
employ hierarchical clustering. Based on the estimated mixing system,
the source signals are estimated in the second stage. The solution for
the second stage utilizes the common assumption of independent and
identically distributed sources. Modeling the sources by a Laplacian
distribution leads to ℓ1-norm minimization.©Springer


FAU Authors / FAU Editors

Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Winter, S., Kellermann, W., Sawada, H., & Makino, S. (2007). Underdetermined blind source separation of convolutive mixtures by hierarchical clustering and L1-norm minimization. In S. Makino, Te-Won Lee, H. Sawada (Eds.), Blind Speech Separation. (pp. 271-304). Dordrecht: Springer.

MLA:
Winter, Stefan, et al. "Underdetermined blind source separation of convolutive mixtures by hierarchical clustering and L1-norm minimization." Blind Speech Separation. Ed. S. Makino, Te-Won Lee, H. Sawada, Dordrecht: Springer, 2007. 271-304.

BibTeX: 

Last updated on 2019-27-05 at 20:22