M. S. Pedersen, J. Larsen, U. Kjems, and L. C. Parra, A survey of convolutive blind source separation, 2006.

S. Makino, H. Sawada, R. Mukai, and S. Araki, Blind Source Separation of Convolutive Mixtures of Speech in Frequency Domain, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.88, issue.7, pp.1640-1655, 2005.
DOI : 10.1093/ietfec/e88-a.7.1640

A. J. Bell and T. J. Sejnowski, An Information-Maximization Approach to Blind Separation and Blind Deconvolution, Neural Computation, vol.20, issue.1, pp.1129-1159, 1995.
DOI : 10.1109/78.301850

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.696

P. Smaragdis, Blind separation of convolved mixtures in the frequency domain, Neurocomputing, vol.22, issue.1-3, pp.21-34, 1998.
DOI : 10.1016/S0925-2312(98)00047-2

M. Z. Ikram and D. R. Morgan, A beamforming approach to permutation alignment for multichannel frequency-domain blind source separation, Proc. of ICASSP, pp.881-884, 2002.

N. Murata, S. Ikeda, and A. Ziehe, An approach to blind source separation based on temporal structure of speech signals, Neurocomputing, vol.41, issue.1-4, 1998.
DOI : 10.1016/S0925-2312(00)00345-3

H. Sawada, R. Mukai, S. Araki, and S. Makino, A Robust and Precise Method for Solving the Permutation Problem of Frequency-Domain Blind Source Separation, IEEE Transactions on Speech and Audio Processing, vol.12, issue.5, pp.530-538, 2004.
DOI : 10.1109/TSA.2004.832994

D. Donoho, For most large underdetermined systems of equations, the minimal ???1-norm near-solution approximates the sparsest near-solution, Communications on Pure and Applied Mathematics, vol.50, issue.7, pp.907-934, 2006.
DOI : 10.1002/cpa.20131