Comparison of superimposition and sparse models in blind source separation by multichannel Wiener filter

@article{Sakanashi2012ComparisonOS,
  title={Comparison of superimposition and sparse models in blind source separation by multichannel Wiener filter},
  author={Ryutaro Sakanashi and Shigeki Miyabe and Takeshi Yamada and Shoji Makino},
  journal={Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference},
  year={2012},
  pages={1-6}
}
Multichannel Wiener filter proposed by Duong et al: can conduct underdetermined blind source separation (BSS) with low distortion. This method assumes that the observed signal is the superimposition of the multichannel source images generated from multivariate normal distributions. The covariance matrix in each time-frequency slot is estimated by an EM algorithm which treats the source images as the hidden variables. Using the estimated parameters, the source images are separated as the maximum… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 12 references

Statistical estimation theory considering time-varying nature of systems and source-probability distributions,

  • M. Togami
  • Ph. D. thesis, the University of Tokyo,
  • 2011
1 Excerpt

Recent advances in audio source separation techniques,

  • H. Sawada, S. Araki, S. Makino
  • J. IEICE,
  • 2008
1 Excerpt

Blind speech separation by combining beamformers and a time frequency binary mask,

  • J. Cermak, S. Araki, H. Sawada, S. Makino
  • Proc. IWAENC,
  • 2006
1 Excerpt

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