Low-artifact Source Separation Using Probabilistic Latent Component Analysis
2013 (English)In: 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE Signal Processing Society, 2013, 6701837- p.Conference paper (Refereed)
We propose a method based on the probabilistic latent componentanalysis (PLCA) in which we use exponential distributions as priorsto decrease the activity level of a given basis vector. A straightforwardapplication of this method is when we try to extract a desiredsource from a mixture with low artifacts. For this purpose, we proposea maximum a posteriori (MAP) approach to identify the commonbasis vectors between two sources. A low-artifact estimate cannow be obtained by using a constraint such that the common basisvectors in the interfering signal’s dictionary tend to remain inactive.We discuss applications of this method in source separationwith similar-gender speakers and in enhancing a speech signal thatis contaminated with babble noise. Our simulations show that theproposed method not only reduces the artifacts but also increasesthe overall quality of the estimated signal.
Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013. 6701837- p.
, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, ISSN 1931-1168
Source Separation, Nonnegative Matrix Factorization (NMF), PLCA, Dictionary Learning, Artifact Reduction
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-124641DOI: 10.1109/WASPAA.2013.6701837ISI: 000349479800029ScopusID: 2-s2.0-84893559774ISBN: 978-147990972-8OAI: oai:DiVA.org:kth-124641DiVA: diva2:637930
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 20 Oct - 23 Oct 2013,New Paltz, New York, U.S.A
QC 201307242013-07-242013-07-242015-12-07Bibliographically approved