Blind separation of sources based on their time-frequency signatures
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Joint anti-diagonalization for blind source separation
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Separating more sources than sensors using time-frequency distributions
EURASIP Journal on Applied Signal Processing
Bilinear signal synthesis in array processing
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Subspace analysis of spatial time-frequency distribution matrices
IEEE Transactions on Signal Processing
Spatial averaging of time-frequency distributions for signalrecovery in uniform linear arrays
IEEE Transactions on Signal Processing
Frequency domain blind MIMO system identification based on second and higher order statistics
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
FPGA implementation of time-frequency analysis algorithms for laser welding monitoring
Microprocessors & Microsystems
A new blind method for separating M+1 sources from M mixtures
Computers & Mathematics with Applications
Blind source separation based on high-resolution time-frequency distributions
Computers and Electrical Engineering
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Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localized in the time-frequency (t-f) domain. In this paper, we propose the use of STFD matrices for both whitening and recovery of the mixing matrix, which are two stages commonly required in many BSS methods, to provide robust BSS performance to noise. In addition, a simple method is proposed to select the auto-and cross-term regions of time-frequency distribution (TFD). To further improve the BSS performance, t-f grouping techniques are introduced to reduce the number of signals under consideration, and to allow the receiver array to separate more sources than the number of array sensors, provided that the sources have disjoint t-f signatures. With the use of one or more techniques proposed in this paper, improved performance of blind separation of nonstationary signals can be achieved.