### RNNoise: Learning Noise Suppression

Sep. 26th, 2017 10:24 pm**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!).

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

Date: 2018-01-17 06:49 pm (UTC)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.