Fast MVAE: Joint Separation and Classification of Mixed Sources Based on Multichannel Variational Autoencoder with Auxiliary Classifier

Li Li, Hirokazu Kameoka, Shoji Makino

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

13 Citations (Scopus)

Abstract

This paper proposes an alternative algorithm for the multi-channel variational autoencoder (MVAE), a recently proposed multichannel source separation approach. While MVAE is notable for its impressive source separation performance, its convergence-guaranteed optimization algorithm and the fact that it allows us to estimate source-class labels simultaneously with source separation, there are still two major drawbacks, namely, the high computational complexity and the unsatisfactory source classification accuracy. To overcome these drawbacks, the proposed method employs an auxiliary classifier VAE, which is an information-theoretic extension of the conditional VAE, for learning the generative model of the source spectrograms. Furthermore, with the trained auxiliary classifier, we introduce a novel algorithm for the optimization that can both reduce the computational time and improve the source classification performance. We call the proposed method fast MVAE (fMVAE) . Experimental evaluations revealed that fMVAE achieved source separation performance comparable to that of MVAE and a source classification accu-racy rate of about 80% while reducing computational time by about 93%.

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.
Pages546-550
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
Country/TerritoryUnited Kingdom
CityBrighton
Period19/5/1219/5/17

Keywords

  • Multichannel source separation
  • auxiliary classifier
  • multi-channel variational autoencoder
  • source classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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