Bayesian inference for nonnegative matrix factor deconvolution models

Serap Kirbiz*, Ali Taylan Cemgil, Bilge Günsel

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

In this paper we develop a probabilistic interpretation and a full Bayesian inference for non-negative matrix deconvolution (NMFD) model. Our ultimate goal is unsupervised extraction of multiple sound objects from a single channel auditory scene. The proposed method facilitates automatic model selection and determination of the sparsity criteria. Our approach retains attractive features of standard NMFD based methods such as fast convergence and easy implementation. We demonstrate the use of this algorithm in the log-frequency magnitude spectrum domain, where we employ it to perform model order selection and control sparseness directly.

Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages2812-2815
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 23 Aug 201026 Aug 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference2010 20th International Conference on Pattern Recognition, ICPR 2010
Country/TerritoryTurkey
CityIstanbul
Period23/08/1026/08/10

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