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Bayesian anisotropic Gaussian model for audio source separation

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


Original languageEnglish
Title of host publication 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
ISBN (Electronic)978-1-5386-4657-1
Publication statusPublished - Apr 2018
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech and Signal Processing - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

ISSN (Electronic)2379-190X


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing


In audio source separation applications, it is common to model the sources as circular-symmetric Gaussian random variables, which is equivalent to assuming that the phase of each source is uniformly distributed. In this paper, we introduce an anisotropic Gaussian source model in which both the magnitude and phase parameters are modeled as random variables. In such a model, it becomes possible to promote a phase value that originates from a signal model and to adjust the relative importance of this underlying model-based phase constraint. We conduct Bayesian inference of the model through the derivation of an expectation-maximization algorithm for estimating the parameters. Experiments conducted on realistic music songs for a monaural source separation task, in an scenario where the variance parameters are assumed known, show that the proposed approach outperforms state-of-the-art techniques.

Publication forum classification

Field of science, Statistics Finland