NMF-based Target Source Separation Using Deep Neural Network

@article{Kang2015NMFbasedTS,
  title={NMF-based Target Source Separation Using Deep Neural Network},
  author={Tae Gyoon Kang and Kisoo Kwon and Jong Won Shin and Nam Soo Kim},
  journal={IEEE Signal Processing Letters},
  year={2015},
  volume={22},
  pages={229-233}
}
Non-negative matrix factorization (NMF) is one of the most well-known techniques that are applied to separate a desired source from mixture data. In the NMF framework, a collection of data is factorized into a basis matrix and an encoding matrix. The basis matrix for mixture data is usually constructed by augmenting the basis matrices for independent sources. However, target source separation with the concatenated basis matrix turns out to be problematic if there exists some overlap between the… CONTINUE READING
Highly Cited
This paper has 30 citations. REVIEW CITATIONS
23 Citations
24 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 23 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 24 references

Similar Papers

Loading similar papers…