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What is currently the most accurate Voice Activity Detection algorithm with high levels of background noise? I would also appreciate if it does not require any manual calibration. Bonus points for examples.

EDIT: I am referring to accuracy as yielding the lowest amount of false positives and negatives.

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  • $\begingroup$ What sort of background noise? What sort of voices (talking? singing?) ? $\endgroup$
    – Peter K.
    Commented Nov 17, 2013 at 18:58
  • $\begingroup$ Talking. Ambient background noise. $\endgroup$
    – Skylion
    Commented Nov 17, 2013 at 19:03
  • $\begingroup$ As the fact that the question is put on hold, I disagree. Accuracy is a quantitative not qualitative measurement. -_- $\endgroup$
    – Skylion
    Commented Nov 17, 2013 at 19:03
  • $\begingroup$ Please re-word (edit) your question to make the accuracy quantitative. What measure are you using? $\endgroup$
    – Peter K.
    Commented Nov 17, 2013 at 19:04
  • $\begingroup$ Is that better? $\endgroup$
    – Skylion
    Commented Nov 17, 2013 at 19:06

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If you have more than 1 detectors, you may try blind separation based on the power spectrum difference of voice and background noise

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  • $\begingroup$ Define more than 1 detectors? As in stereo vs mono? $\endgroup$
    – Skylion
    Commented Nov 27, 2013 at 17:15

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