Monaural ICA of white noise mixtures is hard 
Lars Kai Hansen, Kaare Brandt Petersen

Abstract  Separation of monaural linear mixtures of `white' source signals
is fundamentally illposed. 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 multimodal source reconstruction is
possible with low error, while source signals from the typical
`long tailed' distributions used in many ICA settings can not be
reconstructed. We provide a qualitative discussion of the limits
of monaural blind separation of white noise signals and give a set
of `no go' cases. 
Type  Conference paper [With referee] 
Conference  Proceedings of ICA'2003 Fourth Int. Symp.. on Independent Component Analysis and Blind Signal Separation, Nara Japan, April 4, 
Year  2003 pp. 815820 
Electronic version(s)  [pdf] 
BibTeX data  [bibtex] 
IMM Group(s)  Intelligent Signal Processing 