MAP-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and l(1)-norm minimization

Journal article


Publication Details

Author(s): Winter S, Kellermann W, Sawada H, Makino S
Journal: EURASIP Journal on Advances in Signal Processing
Publisher: Hindawi Publishing Corporation / Springer Verlag (Germany) / SpringerOpen
Publication year: 2007
Volume: 2007
Pages range: 1-12
ISSN: 1687-6172
eISSN: 1687-6180


Abstract


We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures, we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori ( MAP) approach. In the first step, we estimate the mixing matrix based on hierarchical clustering, assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the l(1)-norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming (SOCP) approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers, its results are comparable to, or even better than, the SOCP solution. The advantage is a lower computational cost for problems with low input/output dimensions. Copyright (C) 2007 Stefan Winter et al.



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). MAP-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and l(1)-norm minimization. EURASIP Journal on Advances in Signal Processing, 2007, 1-12. https://dx.doi.org/10.1155/2007/24717

MLA:
Winter, Stefan, et al. "MAP-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and l(1)-norm minimization." EURASIP Journal on Advances in Signal Processing 2007 (2007): 1-12.

BibTeX: 

Last updated on 2018-19-04 at 03:36