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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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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 ( 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.


Date: 2017-10-07 11:44 am (UTC)
From: (Anonymous)
I'd love to see a ladspa-plugin, so that I could watch Youtube-videos & university-lectures denoised with mpv.
Anyone any ideas how hard it would be to create a ladspa-plugin based on it?

I never done anything like this (but have basic C/C++-knowleged)…
From: (Anonymous)
I was wondering myself, if you use a typical FFT\bark.scale Algorhythm on the A.I Enhanced granular wavetable frequency bands, as virtual Bin (size?) calculated predominantly by of course the FFT\barki.scale relying for its final or real.time results, which are limited by or from mainly the CPU/RAM/MEMORY Capacity.
Do you have a Unique (Transform Algorhythm) that uses the "keras" or "theano" CNTK Binary libraries? say something that could be like a hybrid of the FFT & GFT, DTMF? I understand that using a GFT\bark.scale Transfrom Algorhythm alone, could be a massive difference in data\frequency band to using a FFT, as they can potentially use unlimited/Infinite Granular Wavetable band Resolution Virtual Bin data-size/processing power per noise scale or frequency bands!
Understanding there is not large amounts of people, that acknowledge/understand the difference of those two Transform Algorhythms as to begin with.


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