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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Possible Use Cases

[(Anonymous)]

Great Work you are doing Jean Marc!
I was wondering if this could be applicable to active noise cancellation as well, with a real time algorithm say from a raspberry pi or maybe a hardware implementation on a fpga. Of course, this would have to be trained on a different training set; I was thinking the Aurora II? Could it be paired with another algorithm such as beamforming to also attenuate noise not from a specific relative location?

Sorry for the plethora of questions.
Thanks.

Re: Possible Use Cases

[jmvalin]

Active noise cancellation is actually a totally different problem, so unfortunately none of this RNNoise work is applicable.

Re: Possible Use Cases

[(Anonymous)]

Hi Jean,

For active noise cancellation, can you suggest on the approach? Also, is there any project to your knowledge around that?

Re: Possible Use Cases

[jmvalin]

No idea, you should look at the scientific literature on the topic. I don't think deep learning is going to be so useful for that. Or at least not in any form that resembles RNNoise.