Joint Separation and Dereverberation of Reverberant Mixtures with Multichannel Variational Autoencoder

Shota Inoue, Hirokazu Kameoka, Li Li, Shogo Seki, Shoji Makino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper, we deal with a multichannel source separation problem under a highly reverberant condition. The multichan- nel variational autoencoder (MVAE) is a recently proposed source separation method that employs the decoder distribu- tion of a conditional VAE (CVAE) as the generative model for the complex spectrograms of the underlying source sig- nals. Although MVAE is notable in that it can significantly improve the source separation performance compared with conventional methods, its capability to separate highly rever- berant mixtures is still limited since MVAE uses an instan- taneous mixture model. To overcome this limitation, in this paper we propose extending MVAE to simultaneously solve source separation and dereverberation problems by formulat- ing the separation system as a frequency-domain convolutive mixture model. A convergence-guaranteed algorithm based on the coordinate descent method is derived for the optimiza- tion. Experimental results revealed that the proposed method outperformed the conventional methods in terms of all the source separation criteria in highly reverberant environments.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-100
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - 2019 May
Externally publishedYes
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 2019 May 122019 May 17

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period19/5/1219/5/17

Keywords

  • blind derever- beration
  • Blind source separation
  • multichannel audio signal processing
  • multichannel variational autoencoder (MVAE)

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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