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 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. |
| Type | Conference paper [With referee] |
| Conference | International Conference on Acoustics, Speech and Signal Processing |
| Year | 2006 Vol. 5 pp. 657-660 |
| Publisher | Informatics and Mathematical Modelling, Technical University of Denmark, DTU |
| Address | Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby |
| Electronic version(s) | [pdf] [ps] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |