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

Li Li, Hirokazu Kameoka, Shoji Makino

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

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%.

本文言語English
ホスト出版物のタイトル2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ546-550
ページ数5
ISBN(電子版)9781479981311
DOI
出版ステータスPublished - 2019 5月
外部発表はい
イベント44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
継続期間: 2019 5月 122019 5月 17

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2019-May
ISSN(印刷版)1520-6149

Conference

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

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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