@CONFERENCE\{IMM2006-04321,
    author       = "R. K. Olsson and L. K. Hansen",
    title        = "Blind separation of more sources than sensors in convolutive mixtures",
    year         = "2006",
    pages        = "657-660",
    booktitle    = "International Conference on Acoustics, Speech and Signal Processing",
    volume       = "5",
    series       = "",
    editor       = "",
    publisher    = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    organization = "",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4321-full.html",
    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 well-known robust
algorithms that are suited for equal number of sources and
sensors. It is assumed that the sources are non-stationary and
sparsely distributed in the time-frequency 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."
}