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This demo presents the RNNoise project, showing how deep learning can be applied to noise suppression. The main idea is to combine classic signal processing with deep learning to create a real-time noise suppression algorithm that's small and fast. No expensive GPUs required — it runs easily on a Raspberry Pi. The result is much simpler (easier to tune) and sounds better than traditional noise suppression systems (been there!).

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Possible alternative uses for this algorithm ?

Date: 2017-09-29 07:49 am (UTC)
From: (Anonymous)
Long time ago I used to make amateur remixes, and one tricky part was to isolate vocals from the remixed track. To do that I was using the noise removal tool: select a part of the track without vocals, run a spectral analysis on it and then substract the result to the whole track. Most of the time the result was terribly mangled, but sometimes I got something usable.

Your demo got me thinking: if I want to remove something very specific from one track instead of learning a generalized filter, can I train this model with a smaller dataset, like a few seconds from that track?

Fab!

Date: 2017-09-29 03:34 pm (UTC)
From: (Anonymous)
This is great, thanks for your work! Do you think this might be of use with non-audio data?

What about reducing noise in music?

Date: 2017-10-02 03:56 pm (UTC)
From: (Anonymous)
Do you think this approach would be helpful in reducing noise on old 78 rpm records? Because archive.org's Great 78 project has a large number of albums you could experiment with.

Date: 2017-10-03 01:36 pm (UTC)
From: (Anonymous)
Hi Jean Marc,

Impressive Work! Really nice results too. I've been working on a similar project but to be exclusively used with Ardour so it's an lv2 plugin (https://github.com/lucianodato/noise-repellent). I used ME method (Rangachari and Loizou) to estimate noise with it but this method seems to work miles better. Do you mind if I make an lv2 plugin out of your library?
Also did you evaluate using discrete wavelet transform instead of fft+bark scale? I've read in the past few works that get near zero latency with that architecture.
Thank you very much for this. It already taught me few things wasn't aware of.

RESIDUAL NOISE PROBLEM

Date: 2017-10-09 08:21 am (UTC)
From: (Anonymous)
Hi Jean Marc,
According to the audio samples you provided, it seems that the RNNoise has more residual noise than the speex. Do you think it will perform better with more noise samples for training?

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