Blind separation with unknown number of sources based on auto-trimmed neural network

@article{Sun2008BlindSW,
  title={Blind separation with unknown number of sources based on auto-trimmed neural network},
  author={Tsung-Ying Sun and C W Liu and Sheng-Ta Hsieh and Shang-Jeng Tsai},
  journal={Neurocomputing},
  year={2008},
  volume={71},
  pages={2271-2280}
}
This paper focuses on blind source separation with an unknown number of sources, which is the case generally assumed in most practical applications. Several over-determined neural algorithms (more sensors m than sources n) have been proposed to solve the problems associated with these cases, but separating performance is often sacrificed in order to prevent divergence. The general natural gradient descent can be validly applied to determined algorithms (m 1⁄4 n) only. Therefore, to better solve… CONTINUE READING

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