Blind separation of more sources than sensors in convolutive mixtures 
Rasmus Kongsgaard Olsson, Lars Kai Hansen

Abstract  We demonstrate that blind separation of more sources than sensors
can be performed based solely on the second order statistics of
the observed mixtures. This a generalization of wellknown robust
algorithms that are suited for equal number of sources and
sensors. It is assumed that the sources are nonstationary and
sparsely distributed in the timefrequency plane. The mixture
model is convolutive, i.e. acoustic setups such as the cocktail
party problem are contained. The limits of identifiability are
determined in the framework of the PARAFAC model. In the
experimental section, it is demonstrated that real room recordings
of 3 speakers by 2 microphones can be separated using the method. 
Type  Conference paper [With referee] 
Conference  International Conference on Acoustics, Speech and Signal Processing 
Year  2006 Vol. 5 pp. 657660 
Publisher  Informatics and Mathematical Modelling, Technical University of Denmark, DTU 
Address  Richard Petersens Plads, Building 321, DK2800 Kgs. Lyngby 
Electronic version(s)  [pdf] [ps] 
BibTeX data  [bibtex] 
IMM Group(s)  Intelligent Signal Processing 