A Bayesian hierarchical model for blind audio source separation

Yaron Laufer, Sharon Gannot

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

1 Scopus citations

Abstract

This paper presents a fully Bayesian hierarchical model for blind audio source separation in a noisy environment. Our probabilistic approach is based on Gaussian priors for the speech signals, Gamma hyperpriors for the speech precisions and a Gamma prior for the noise precision. The time-varying acoustic channels are modelled with a linear-Gaussian state-space model. The inference is carried out using a variational Expectation-Maximization (VEM) algorithm, leading to a variant of the multi-speaker multichannel Wiener filter (MCWF) to separate and enhance the audio sources, and a Kalman smoother to infer the acoustic channels. The VEM speech estimator can be decomposed into two stages: A multi-speaker linearly constrained minimum variance (LCMV) beamformer followed by a variational multi-speaker postfilter. The proposed algorithm is evaluated in a static scenario using recorded room impulse responses (RIRs) with two reverberation levels, showing superior performance compared to competing methods.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages276-280
Number of pages5
ISBN (Electronic)9789082797053
DOIs
StatePublished - 24 Jan 2021
Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
Duration: 24 Aug 202028 Aug 2020

Publication series

NameEuropean Signal Processing Conference
Volume2021-January
ISSN (Print)2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
Country/TerritoryNetherlands
CityAmsterdam
Period24/08/2028/08/20

Bibliographical note

Publisher Copyright:
© 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

Funding

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme, Grant Agreement No. 871245. Yaron Laufer is supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme, Grant Agreement No. 871245. Yaron Laufer is supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities.

FundersFunder number
Horizon 2020 Framework Programme
Israel Academy of Sciences and Humanities
Horizon 2020871245

    Keywords

    • Audio source separation
    • Variational EM

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