Blind source separation with perceptual post processing

@article{Parikh2011BlindSS,
  title={Blind source separation with perceptual post processing},
  author={Devangi N. Parikh and David V. Anderson},
  journal={2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE)},
  year={2011},
  pages={321-325}
}
In an environment with multiple audio sources, blind source separation (BSS) makes use of multiple microphone signals to estimate the respective source signals. Under normal circumstances, it is not possible to completely “unmix” the audio sources. One technique to further improve the system performance is to use all BSS outputs to generate a Wiener filter that is then applied to the desired output. The Wiener post processing improves the signal-to-interference ratio (SIR) but we show that it… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-7 OF 7 REFERENCES

Residual cross-talk suppression for convolutive Blind Source Separation

  • 2010 2nd International Conference on Computer Engineering and Technology
  • 2010
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Gain adaptation based on signal-to-noise ratio for noise suppression

  • 2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
  • 2009
VIEW 2 EXCERPTS

Parikh , Muhammad Ikram , and David V . Anderson , “ Implementation of blind source separation and a post - processing algorithm for noise suppression in cell - phone applications

N. Devangi
  • IEEE International Conference on Acoustics , Speech , and Signal Processing

Parikh , Sourabh Ravindran , and David V . Anderson , “ Gain adaptation based on signal - to - noise ratio for noise suppression

N. Devangi
  • IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

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