Separating more sources than sensors using time-frequency distributions
EURASIP Journal on Applied Signal Processing
Blind separation of nonstationary sources based on spatial time-frequency distributions
EURASIP Journal on Applied Signal Processing
Underdetermined blind source separation based on relaxed sparsity condition of sources
IEEE Transactions on Signal Processing
Underdetermined blind separation of non-sparse sources using spatial time-frequency distributions
Digital Signal Processing
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Non unitary joint block diagonalization of complex matrices using a gradient approach
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A new blind method for separating M+1 sources from M mixtures
Computers & Mathematics with Applications
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We address the problem of blind source separation of non-stationary signals of which only instantaneous linear mixtures are observed. A blind source separation approach exploiting both auto-terms and cross-terms of the time-frequency (TF) distributions of the sources is considered. The approach is based on the simultaneous diagonalization and anti-diagonalization of spatial TF distribution matrices made up of, respectively, auto-terms and cross-terms. Numerical simulations are provided to demonstrate the effectiveness of the proposed approach and compare its performances with existing TF-based methods.