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Exactly my thought, with using their own service! The thing was, is that the background noise made the API not recognise the actual words (I think - since you c

Exactly my thought, with using their own service! The thing was, is that the background noise made the API not recognise the actual words (I think - since you can't really "debug"), but maybe if you reduce the noise, it might be possible - I wanted to give that a go, but haven't had a chance yet. I just don't know of any ways to automatically reduce noise.

deleted 14y ago
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coldpie 14y ago
I suspect you haven't gotten around to it, but did you study the noise at all? According to Wikipedia <http://en.wikipedia.org/wiki/Voice_frequency> http://en.wikipedia.org/wiki/Voice_frequency>, 85-255 Hz is the usual range of human speech. You could try a band-pass filter with that pass band, and probably a pretty high roll-off (this will cut off sounds like S and X, but I suspect voice recognition software is often capable of dealing with those, given how often they fail to be represented well in discrete audio). Audacity can do this, and there's almost certainly automatible tools out there. Worth a shot, anyway.
- dutchbrit 14y ago
  I found Audicity when I was playing around, but I didn't get round to actually trying it. Didn't know about the frequency so thanks!!!
  - goostavos 14y ago
    The coursera Machine Learning course references an algorithm that can separate speech from noise, which a fairly high degree of accuracy. May be worth looking into. Noise removal tools like audacity tend to introduce "watery" or "bubbly" effects when pushed hard.
- Dn_Ab 14y ago
  There must be a reason why they are confident current algorithms will have a hard time. The article says it works by playing speech backwards so any basic filter will also significantly distort the speech part since I assume the "noise" will occupy similar bands. Subtractive techniques probably won't work either because the noise will likely be highly varying. But perhaps if one could learn two sparse representations that incorporated phase information for backwards and forwards speech using matrix factorization - it might work as a method for removing recaptcha noise. This idea is so simple though, I assume someone tried it and it doesn't work well.

deleted 14y ago

[deleted]

coldpie 14y ago

I suspect you haven't gotten around to it, but did you study the noise at all? According to Wikipedia <http://en.wikipedia.org/wiki/Voice_frequency> http://en.wikipedia.org/wiki/Voice_frequency>, 85-255 Hz is the usual range of human speech. You could try a band-pass filter with that pass band, and probably a pretty high roll-off (this will cut off sounds like S and X, but I suspect voice recognition software is often capable of dealing with those, given how often they fail to be represented well in discrete audio). Audacity can do this, and there's almost certainly automatible tools out there. Worth a shot, anyway.

dutchbrit 14y ago
I found Audicity when I was playing around, but I didn't get round to actually trying it. Didn't know about the frequency so thanks!!!
- goostavos 14y ago
  The coursera Machine Learning course references an algorithm that can separate speech from noise, which a fairly high degree of accuracy. May be worth looking into. Noise removal tools like audacity tend to introduce "watery" or "bubbly" effects when pushed hard.
Dn_Ab 14y ago
There must be a reason why they are confident current algorithms will have a hard time. The article says it works by playing speech backwards so any basic filter will also significantly distort the speech part since I assume the "noise" will occupy similar bands. Subtractive techniques probably won't work either because the noise will likely be highly varying. But perhaps if one could learn two sparse representations that incorporated phase information for backwards and forwards speech using matrix factorization - it might work as a method for removing recaptcha noise. This idea is so simple though, I assume someone tried it and it doesn't work well.