Bayesian group sparse learning for music source separation

@article{Chien2013BayesianGS,
  title={Bayesian group sparse learning for music source separation},
  author={Jen-Tzung Chien and Hsin-Lung Hsieh},
  journal={EURASIP J. Audio, Speech and Music Processing},
  year={2013},
  volume={2013},
  pages={18}
}
Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative signals with the sparseness constraint. The signals are adequately represented by a set of basis vectors and the corresponding weight parameters. NMF has been successfully applied for blind source separation and many other signal processing systems. Typically, controlling the degree of sparseness and characterizing the uncertainty of model parameters are two critical issues for model regularization… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 31 references

A Mnih, in Proceedings of the International Conference on Machine Learning (ICML). Bayesian probabilistic matrix factorization using Markov chain Monte Carlo (Helsinki

  • R Salakhutdinov
  • 2008
Highly Influential
8 Excerpts

Carteret, Separation of non-negative mixture of non-negative sources using a Bayesian approach and MCMC sampling

  • S Moussaoui, D Brie, C Mohammad-A Djafari
  • IEEE Trans. Signal Process
  • 2006
Highly Influential
9 Excerpts

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