Monaural Ica of White Noise Mixtures Is Hard

  title={Monaural Ica of White Noise Mixtures Is Hard},
  author={Lars Kai Hansen and Kaare Brandt Petersen},
Separation of monaural linear mixtures of ‘white’ source signals is fundamentally ill-posed. In some situations it is not possible to find the mixing coefficients for the full ‘blind’ problem. If the mixing coefficients are known, the structure of the source prior distribution determines the source reconstruction error. If the prior is strongly multi-modal source reconstruction is possible with low error, while source signals from the typical ‘long tailed’ distributions used in many ICA… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 19 references

Blind Separation of Single Channel Mixture Using ICA Basis Functions

View 3 Excerpts

Kidmose : Alphastable distributions in signal processing of audio signals

proceedings of the 41 ’ st Conference on Simulation and Modelling , Scandinavian Simulation Society • 2000

On the Independent Components of Functional Neuroimages

K. Petersen, L. K. Hansen, T. Kolenda, E. Rostrup, S. Strother
In Proceedings of ICA-2000 • 2000
View 1 Excerpt

Sejnowski: Learning overcomplete representations Neural Computation

T.J.M.S. Lewicki
View 3 Excerpts

Rosca: AR processes and sources can be reconstructed from degenerate mixtures

R. Balan, J.A
Proceedings of the First International Conference on Independent Component Analysis and Blind Source Separation ICA’99, • 1999
View 2 Excerpts

Independent Component Analysis: Theory and Applications

T.-W. Lee
Kluwer Academic Publ. Bostin • 1998
View 2 Excerpts

Similar Papers

Loading similar papers…