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

Input and output data dimensions

[(Anonymous)]

Dear Jean-Marc,

Many thanks for publishing your exciting work and sharing your code.
I've two points which are not 100% clear to me after reading your documentations and code:

(1) Network training input and output data samples are finite sequences of 42- and 23-element vectors, respectively. But in the operation mode, the trained network is fed sequentially with a single input vector and outputs a single vector?

(2) Is the training data extracted from overlapping spectrogram segments?

Kind regards

Re: Input and output data dimensions

[jmvalin]

1) For training, the RNN needs to be fed a full sequence so that it can backpropagate through time. When we actually use the RNN, we also feed a sequence, but we do it one frame at a time because we want the algorithm to be real-time (for batch denoising, we could feed the sequence all at once)

2) Yes, we use a frame size of 20 ms, with 10 ms overlap.

Re: Input and output data dimensions

[(Anonymous)]

Thanks a lot for your quick answer!
Regarding 2), I think I have to specify my question:

Looking at your training code (rnnoise/training/rnn_train.py), you feed the network with sequences of 2000 42-element vectors/frames (= 1 training sample). Now I wonder if two distinct training samples might share a certain number of frames?