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Bayesian Recursive Blind Source Separation
KTH, School of Electrical Engineering (EES), Communication Theory.
KTH, School of Electrical Engineering (EES), Communication Theory.
(English)In: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928Article in journal (Other academic) Submitted
Abstract [en]

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
MIT Press.
Keyword [en]
Blind Source Separation, Two-dimensional Hidden Markov Models, Bayesian Learning, Variational Inference, Watson Distribution
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-153783OAI: oai:DiVA.org:kth-153783DiVA: diva2:753690
Note

QS 2014

Available from: 2014-10-08 Created: 2014-10-08 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Bayesian Modeling of Directional Data with Acoustic and Other Applications
Open this publication in new window or tab >>Bayesian Modeling of Directional Data with Acoustic and Other Applications
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A direction is defined here as a multi-dimensional unit vector. Such unitvectors form directional data. Closely related to directional data are axialdata for which each direction is equivalent to the opposite direction.Directional data and axial data arise in various fields of science. In probabilisticmodeling of such data, probability distributions are needed whichcount for the structure of the space from which data samples are collected.Such distributions are known as directional distributions and axial distributions.This thesis studies the von Mises-Fisher (vMF) distribution and the(complex) Watson distribution as representatives of directional and axialdistributions.Probabilistic models of the data are defined through a set of parameters.In the Bayesian view to uncertainty, these parameters are regarded as randomvariables in the learning inference. The primary goal of this thesis is todevelop Bayesian inference for directional and axial models, more precisely,vMF and (complex) Watson distributions, and parametric mixture modelsof such distributions. The Bayesian inference is realized using a family ofoptimization methods known as variational inference. With the proposedvariational methods, the intractable Bayesian inference problem is cast asan optimization problem.The variational inference for vMF andWatson models shall open up newapplications and advance existing application domains by reducing restrictiveassumptions made by current modelling techniques. This is the centraltheme of the thesis in all studied applications. Unsupervised clustering ofgene-expression and gene-microarray data is an existing application domain,which has been further advanced in this thesis. This thesis also advancesapplication of the complex Watson models in the problem of blind sourceseparation (BSS) with acoustic applications. Specifically, it is shown thatthe restrictive assumption of prior knowledge on the true number of sourcescan be relaxed by the desirable pruning property in Bayesian learning, resultingin BSS methods which can estimate the number of sources.Furthermore, this thesis introduces a fully Bayesian recursive frameworkfor the BSS task. This is an attempt toward realization of an online BSSmethod. In order to reduce the well-known problem of permutation ambiguityin the frequency domain, the complete BSS problem is solved in one unified modeling step, combining the frequency bin-wise source estimationwith the permutation problem. To realize this, all time frames and frequencybins are connected using a first order Markov chain. The model cancapture dependencies across both time frames and frequency bins, simultaneously,using a feed-forward two-dimensional hidden Markov model (2-DHMM).

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. xviii, 63 p.
Series
TRITA-EE, ISSN 1653-5146 ; 2014:043
Keyword
Directional statistics, directional distributions, axial distributions, von Mises-Fisher distribution, complex Watson distribution, complex Bingham distribution, Bayesian inference, probabilistic modeling, variational inference, two-dimensional hidden Markov models, Markov chain, blind source separation, frequency domain BSS, underdetermined BSS, online BSS, Bayesian recursive, gene expression data, gene-microarray data, line spectral frequency, speaker identification. ii
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-153786 (URN)978-91-7595-281-9 (ISBN)
Public defence
2014-10-31, Q2, Osquldasväg 10 (02 tr), KTH, Stockholm, 09:00 (English)
Supervisors
Note

QC 20141009

Available from: 2014-10-09 Created: 2014-10-08 Last updated: 2014-10-09Bibliographically approved

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