Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks

  title={Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks},
  author={Po-Sen Huang and Minje Kim and Mark Hasegawa-Johnson and Paris Smaragdis},
Monaural source separation is important for many real world applications. It is challenging since only single channel information is available. In this paper, we explore using deep recurrent neural networks for singing voice separation from monaural recordings in a supervised setting. Deep recurrent neural networks with different temporal connections are explored. We propose jointly optimizing the networks for multiple source signals by including the separation step as a nonlinear operation in… CONTINUE READING
Highly Cited
This paper has 69 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 48 extracted citations

69 Citations

Citations per Year
Semantic Scholar estimates that this publication has 69 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 17 references

Similar Papers

Loading similar papers…