000165368 001__ 165368
000165368 005__ 20180913060604.0
000165368 037__ $$aCONF
000165368 245__ $$aEstimating multiple filters from stereo mixtures: a double sparsity approach
000165368 269__ $$a2011
000165368 260__ $$c2011
000165368 336__ $$aConference Papers
000165368 520__ $$aWe consider the problem of estimating multiple filters from convolutive mixtures of several unknown sources. We propose to exploit both the time-frequency (TF) sparsity of the sources and the sparsity of the mixing filters. Our framework consists of: a) a clustering step to group the TF points where only one source is active, for each source; b) a convex optimisation step, to estimate the filters using TF cross-relations that capture linear constraints satisfied by the unknown filters. Experiments demonstrate that the approach is well suited for the estimation of sufficiently sparse filters.
000165368 6531_ $$aconvolutive blind source separation
000165368 6531_ $$asparse filter estimation
000165368 6531_ $$al1 minimisation
000165368 6531_ $$aconvex optimisation
000165368 6531_ $$aLTS2
000165368 700__ $$0242928$$g196462$$aArberet, Simon
000165368 700__ $$aSudhakar, Prasad
000165368 700__ $$aGribonval, Rémi$$g169389$$0(EPFLAUTH)169389
000165368 7112_ $$dJune 27-30, 2011$$cEdinburgh, Scotland.$$aWorkshop : Signal Processing with Adaptive Sparse Structured Representations (SPARS)
000165368 773__ $$tWorkshop : Signal Processing with Adaptive Sparse Structured Representations (SPARS)
000165368 909C0 $$xU10380$$0252392$$pLTS2
000165368 909CO $$pconf$$pSTI$$ooai:infoscience.tind.io:165368
000165368 917Z8 $$x196462
000165368 917Z8 $$x120906
000165368 937__ $$aEPFL-CONF-165368
000165368 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000165368 980__ $$aCONF