Blind separation of more sources than sensors in convolutive mixtures

Rasmus Kongsgaard Olsson; Lars Kai Hansen

doi:10.1109/ICASSP.2006.1661361

Blind separation of more sources than sensors in convolutive mixtures

Rasmus Kongsgaard Olsson, Lars Kai Hansen

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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.

Original language	English
Title of host publication	International Conference on Acoustics, Speech and Signal Processing
Volume	5
Publisher	IEEE
Publication date	2006
ISBN (Print)	1-4244-0469-X
DOIs	https://doi.org/10.1109/ICASSP.2006.1661361
Publication status	Published - 2006
Event	2006 IEEE International Conference on Acoustics, Speech and Signal Processing - Toulouse, France Duration: 14 May 2006 → 19 May 2006 Conference number: 31

Conference

Conference	2006 IEEE International Conference on Acoustics, Speech and Signal Processing
Number	31
Country/Territory	France
City	Toulouse
Period	14/05/2006 → 19/05/2006

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Copyright: 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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title = "Blind separation of more sources than sensors in convolutive mixtures",

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.",

author = "Olsson, \{Rasmus Kongsgaard\} and Hansen, \{Lars Kai\}",

note = "Copyright: 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE; 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 ; Conference date: 14-05-2006 Through 19-05-2006",

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DO - 10.1109/ICASSP.2006.1661361

M3 - Article in proceedings

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BT - International Conference on Acoustics, Speech and Signal Processing

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T2 - 2006 IEEE International Conference on Acoustics, Speech and Signal Processing

Y2 - 14 May 2006 through 19 May 2006

ER -

Blind separation of more sources than sensors in convolutive mixtures

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