Learning speech features in the presence of noise: Sparse convolutive robust non-negative matrix factorization

@article{Frein2009LearningSF,
  title={Learning speech features in the presence of noise: Sparse convolutive robust non-negative matrix factorization},
  author={Ruairi de Frein and Scott T. Rickard},
  journal={2009 16th International Conference on Digital Signal Processing},
  year={2009},
  pages={1-6}
}
We introduce a non-negative matrix factorization technique which learns speech features with temporal extent in the presence of non-stationary noise. Our proposed technique, namely Sparse convolutive robust non-negative matrix factorization, is robust in the presence of noise due to our explicit treatment of noise as an interfering source in the factorization. We derive multiplicative update rules using the alpha divergence objective. We show that our proposed method yields superior performance… CONTINUE READING

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