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Source Separation using the Spectral Flatness Measure

 
: Bardeli, Rolf

International Workshop on Machine Listening in Multisource Environments 2011. Proceedings : 1st September 2011, Florence, Italy (satellite event of Interspeech 2011)
Florence, 2011
pp.80-85
International Workshop on Machine Listening in Multisource Environments <2011, Florence>
International Speech Communication Association (Annual Conference INTERSPEECH) <12, 2011, Florence>
English
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
source separation; spectral flatness measure

Abstract
Complex audio scenes with a large number of sound sources pose one of the most difficult problems for audio pattern recognition. Therefore, methods for source separation are very important in this context. Many source separation methods try to exactly recover every source in an audio scene. In this paper, however, we propose an algorithm for the extraction of simpler components from complex audio scenes based on an optimisation approach using a sound complexity measure derived from the spectral flatness measure. We yield good separation for artificial mixtures of three signals with time dependent mixing conditions.

: http://publica.fraunhofer.de/documents/N-194559.html