Blind separation of more sources than sensors in convolutive mixtures

Rasmus Kongsgaard Olsson, Lars Kai Hansen

AbstractWe 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.
TypeConference paper [With referee]
ConferenceInternational Conference on Acoustics, Speech and Signal Processing
Year2006    Vol. 5    pp. 657-660
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
Electronic version(s)[pdf] [ps]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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