Deep unfolding for multichannel source separation

Scott Wisdom, John Hershey, Jonathan Le Roux, Shinji Watanabe

研究成果: Conference contribution

20 被引用数 (Scopus)

抄録

Deep unfolding has recently been proposed to derive novel deep network architectures from model-based approaches. In this paper, we consider its application to multichannel source separation. We unfold a multichannel Gaussian mixture model (MCGMM), resulting in a deep MCGMM computational network that directly processes complex-valued frequency-domain multichannel audio and has an architecture defined explicitly by a generative model, thus combining the advantages of deep networks and model-based approaches. We further extend the deep MCGMM by modeling the GMM states using an MRF, whose unfolded mean-field inference updates add dynamics across layers. Experiments on source separation for multichannel mixtures of two simultaneous speakers shows that the deep MCGMM leads to improved performance with respect to the original MCGMM model.

本文言語English
ホスト出版物のタイトル2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ121-125
ページ数5
ISBN(電子版)9781479999880
DOI
出版ステータスPublished - 2016 5月 18
外部発表はい
イベント41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
継続期間: 2016 3月 202016 3月 25

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2016-May
ISSN(印刷版)1520-6149

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
国/地域China
CityShanghai
Period16/3/2016/3/25

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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