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Donate Your Noise To Xiph/Mozilla's Deep-Learning Noise Suppression Project (xiph.org)
Mozilla-backed researchers are working on a real-time noise suppression algorithm using a neural network -- and they want your noise! Long-time Slashdot reader jmv writes:
The Mozilla Research RRNoise project combines classic signal processing with deep learning, but it's small and fast. No expensive GPUs required -- it runs easily on a Raspberry Pi. The result is easier to tune and sounds better than traditional noise suppression systems (been there!). And you can help!
From the site: Click on this link to let us record one minute of noise from where you are... We're interested in noise from any environment where you might communicate using voice. That can be your office, your car, on the street, or anywhere you might use your phone or computer.
They claim it already sounds better than traditional noise suppression systems, and even though the code isn't optmized yet, "it already runs about 60x faster than real-time on an x86 CPU."
From the site: Click on this link to let us record one minute of noise from where you are... We're interested in noise from any environment where you might communicate using voice. That can be your office, your car, on the street, or anywhere you might use your phone or computer.
They claim it already sounds better than traditional noise suppression systems, and even though the code isn't optmized yet, "it already runs about 60x faster than real-time on an x86 CPU."
(I'm the author of the article)
You may not be aware, but around 10 years ago, browsers started including audio technology. This now includes WebRTC which lets you do videoconferencing in the browser (without Flash). As surprising as it may sound, some people like doing VoIP/videoconference. And those who use WebRTC tend to prefer when their audio doesn't have too much noise. And that is why RNNoise is useful.
Opus: the Swiss army knife of audio codec
The entire project is on github.
I found this by going to the link in TFS.
I've fallen off your lawn, and I can't get up.
It's the first time we try this. We'll look at the quality of the data we get (yes, noise quality!) and if it's sufficiently good/useful, then we'll also make it available. It might take some time to sort out the useful samples from the ones that aren't since some already have noise suppression applied by the OS or browser.
Opus: the Swiss army knife of audio codec
Now, training is a little trickier because I cannot share the data.
I cannot share the current data I'm using because it's copyrighted. Hence asking for people for help getting data that I can redistribute.
So weâ(TM)re supposed to just give jmv a bunch of data with no way to know how he is using it?
Yes, because I have such a track record for keeping things private.
Opus: the Swiss army knife of audio codec