
Applied Neural Networks - lowpro
https://gooddebate.org/2017/12/applied-neural-networks/
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lowpro
Hi everyone! I just finished this post, please enjoy it and let me know if you
have questions or suggestions (especially regarding the math portions, of
which I have a hard time understanding anything beyond year 2 calculus!).

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posterboy
Thinking about noise reduction I come up with low pass or band pass filters
and using multiple microphones to correlate the signal. However I have no
experience, at all.

In the most simple case, simply adding two slightly separated signals should
not increase the noise. The problem is, the helicopter sound itself is very
noisy, so I'm not sure this would work well. A lot of microphones might be
needed, increasing the cost many times.

For low pass filtering, there are mufflers for microphones, did you have
those?

Edit: and of course that's not really getting any points in the computer
science department.

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lowpro
We did think of mufflers, but we didn't have them as we were using a dataset
collected by another researcher, who used the same system in a quiet
environment, then in a windy 'real world' environment. We used this data for
training, testing and validation, but didn't have the equipment or time to
make a new dataset :(

But this is probably the most practical solution, the data we have doesn't
allow for it however. This could be used in the real world and the system
could be more accurate, however in a crowded environment there will be other
sounds the microphone will pick up, so some type of noise filter would be
needed anyway.

~~~
junkcollector
From a purely signal point of view, if you have a good characterization of the
drone sound (your training set) and of the noise, then the appropriate filter
would be something along the lines of a weiner type which statistically
maximizes the SNR. Preventing your microphone from saturating when measuring
the wind is very important as well, and a good use of a wind blocker or
muffler.

If your microphone saturates its input that will distort the spectrum of the
signal you are measuring.

