Bayesian Recursive Blind Source Separation
(English)In: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928Article in journal (Other academic) Submitted
We consider the problem of blind source separation (BSS) of convolutive mixtures in underdeterminedscenarios, where there are more sources to estimate than recorded signals. This problemhas been intensively studied in the literature. Many successful methods relay on batch processingof previously recorded signals, and hence are only best suited for noncausal systems. This paperaddresses the problem of online BSS. To realize this, we develop a Bayesian recursive framework.The proposed Bayesian framework allows incorporating prior knowledge in a coherentway, and therecursive learning allows to combine information gained from the current observation with all informationfromthe previous observations. Experiments using live audio recordings show promisingresults.
Place, publisher, year, edition, pages
Blind Source Separation, Two-dimensional Hidden Markov Models, Bayesian Learning, Variational Inference, Watson Distribution
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-153783OAI: oai:DiVA.org:kth-153783DiVA: diva2:753690
QS 20142014-10-082014-10-082016-02-15Bibliographically approved