A variational Bayes approach to the underdetermined blind source separation with automatic determination of the number of sources
2012 (English)In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on / [ed] IEEE, IEEE , 2012, 253-256 p.Conference paper (Refereed)
In this paper, we propose a variational Bayes approach to the underdetermined blind source separation and show how a variational treatment can open up the possibility of determining the actual number of sources. The procedure is performed in a frequency bin-wise manner. In every frequency bin, we model the time-frequency mixture by a variational mixture of Gaussians with a circular-symmetric complex-Gaussian density function. In the Bayesian inference, we consider appropriate conjugate prior distributions for modeling the parameters of this distribution. The learning task consists of estimating the hyper-parameters characterizing the parameter distributions for the optimization of the variational posterior distribution. The proposed approach requires no prior knowledge on the number of sources in a mixture.
Place, publisher, year, edition, pages
IEEE , 2012. 253-256 p.
, IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
blind source separation, number of sources, variational Bayesian approach, variational mixture of Gaussians
Other Electrical Engineering, Electronic Engineering, Information Engineering Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-75456DOI: 10.1109/ICASSP.2012.6287865ISI: 000312381400063ScopusID: 2-s2.0-84867610385ISBN: 978-146730046-9OAI: oai:DiVA.org:kth-75456DiVA: diva2:490499
2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012; Kyoto; 25 March 2012 through 30 March 2012
FunderICT - The Next Generation
QC 201211192012-02-052012-02-052013-04-15Bibliographically approved