Separation of Unknown Number of Sources
2014 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 21, no 5, 625-629 p.Article in journal (Refereed) Published
We address the problem of blind source separation in acoustic applications where there is no prior knowledge about the number of mixing sources. The presented method employs a mixture of complex Watson distributions in its generative model with a sparse Dirichlet distribution over the mixture weights. The problem is formulated in a fully Bayesian inference with assuming prior distributions over all model parameters. The presented model can regulate its own complexity by pruning unnecessary components by which we can possibly relax the assumption of prior knowledge on the number of sources.
Place, publisher, year, edition, pages
2014. Vol. 21, no 5, 625-629 p.
Bayesian inference, Blind source separation, Complex Watson distribution, Variational inference
IdentifiersURN: urn:nbn:se:kth:diva-153744DOI: 10.1109/LSP.2014.2309607ISI: 000347922600001ScopusID: 2-s2.0-84897498048OAI: oai:DiVA.org:kth-153744DiVA: diva2:753551
QC 201410072014-10-082014-10-082015-01-29Bibliographically approved