RNNoise: Learning Noise Suppression

[jmvalin]

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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Comments

Also did you evaluate using discrete wavelet transform instead of fft+bark scale?

[(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.