Underdetermined Convolutive Blind Source Separation Using a Novel Mixing Matrix Estimation and MMSE-based Source Estimation

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 306
  • Download : 0
This paper considers underdetermined blind source separation of super-Gaussian signals that are convolutively mixed. The separation is performed in three stages. In the first stage, the mixing matrix in each frequency bin is estimated by the proposed single source detection and clustering (SSDC) algorithm. In the second stage, by assuming complex-valued super-Gaussian distribution, the sources are estimated by minimizing a mean-square-error (MSE) criterion. Special consideration is given to reduce computational load without compromising accuracy. In the last stage, the estimated sources in each frequency bin are aligned for recovery. In our simulations, the proposed algorithm outperformed conventional algorithm in terms of the mixing-error-ratio and the signal-to-distortion ratio.
Publisher
IEEE
Issue Date
2011-09
Language
ENG
Citation

IEEE Workshop on Machine Learning for Signal Processing

URI
http://hdl.handle.net/10203/169483
Appears in Collection
EE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0