ACM Computing Surveys (CSUR)
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
A novel approach for underdetermined blind sources separation in frequency domain
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Underdetermined Anechoic Blind Source Separation via -Basis-Pursuit With
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain
IEEE Transactions on Signal Processing
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Batch and Online Underdetermined Source Separation Using Laplacian Mixture Models
IEEE Transactions on Audio, Speech, and Language Processing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
Mixing matrix estimation using discriminative clustering for blind source separation
Digital Signal Processing
An algorithm for underdetermined mixing matrix estimation
Neurocomputing
A fast mixing matrix estimation method in the wavelet domain
Signal Processing
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Sparsity of signals in the frequency domain is widely used for blind source separation (BSS) when the number of sources is more than the number of mixtures (underdetermined BSS). In this paper we propose a simple algorithm for detection of points in the time-frequency (TF) plane of the instantaneous mixtures where only single source contributions occur. Samples at these points in the TF plane can be used for the mixing matrix estimation. The proposed algorithm identifies the single-source-points (SSPs) by comparing the absolute directions of the real and imaginary parts of the Fourier transform coefficient vectors of the mixed signals. Finally, the SSPs so obtained are clustered using the hierarchical clustering algorithm for the estimation of the mixing matrix. The proposed idea for the SSP identification is simpler than the previously reported algorithms.