Blind identification and source separation in 2×3 under-determined mixtures

Authors:
P. Comon
Affiliations:
Lab., Univ. of Nice, Sophia Antipolis, France
Venue:
IEEE Transactions on Signal Processing
Year:
2004

Citing 0
Cited 13

Blind identification of under-determined mixtures based on the characteristic function

Signal Processing - Signal processing in UWB communications
Underdetermined blind audio source separation using modal decomposition

EURASIP Journal on Audio, Speech, and Music Processing
Blind channel identification algorithms based on the Parafac decomposition of cumulant tensors: The single and multiuser cases

Signal Processing
Blind partial separation of underdetermined convolutive mixtures of complex sources based on differential normalized kurtosis

Neurocomputing
Adaptive algorithms to track the PARAFAC decomposition of a third-order tensor

IEEE Transactions on Signal Processing
Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants

IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of dependent sources

ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures

IEEE Transactions on Audio, Speech, and Language Processing
Second-Order blind identification of underdetermined mixtures

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Differential fast fixed-point BSS for underdetermined linear instantaneous mixtures

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind constrained block-Tucker2 receiver for multiuser SIMO NL-CDMA communication systems

Signal Processing
Sparse coding with adaptive dictionary learning for underdetermined blind speech separation

Speech Communication
Most Tensor Problems Are NP-Hard

Journal of the ACM (JACM)

Quantified Score

Hi-index	35.69

Visualization

Abstract

Under-determined mixtures are characterized by the fact that they have more inputs than outputs, or, with the antenna array processing terminology, more sources than sensors. The problem addressed is that of identifying and inverting the mixture, which obviously does not admit a linear inverse. Identification is carried out with the help of tensor canonical decompositions. On the other hand, the discrete distribution of the sources is utilized for performing the source extraction, the under-determined mixture being either known or unknown. The results presented in this paper are limited to two-dimensional (2-D) mixtures of three sources.