jmvalin: (Default)
[personal profile] 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!).

Read More

Possible alternative uses for this algorithm ?

Date: 2017-09-29 07:49 am (UTC)
From: (Anonymous)
Long time ago I used to make amateur remixes, and one tricky part was to isolate vocals from the remixed track. To do that I was using the noise removal tool: select a part of the track without vocals, run a spectral analysis on it and then substract the result to the whole track. Most of the time the result was terribly mangled, but sometimes I got something usable.

Your demo got me thinking: if I want to remove something very specific from one track instead of learning a generalized filter, can I train this model with a smaller dataset, like a few seconds from that track?
From: (Anonymous)
Hi Jean Marc,

There was a 1.6 million $ Indigogo project about Snoring noise suppression device that went bust.

Do you think you could help them?


Date: 2017-09-29 03:34 pm (UTC)
From: (Anonymous)
This is great, thanks for your work! Do you think this might be of use with non-audio data?

Re: Fab!

Date: 2017-10-02 12:27 am (UTC)
From: (Anonymous)
Hi this looks really interesting. I am working with a team to try to improve optical character recognition on old and "noisy" text images that humans can read but OCR cannot. We have been using neural net to do this with some success on training data (, my github username is rcrath) using OCRopy but I have always thought treating the text as a data stream and using department to get a noise sample of the garbage text that OCR makes when it fails and subtracting that from the signal might reduce the garbage in an OCR file to make other approaches better focused. I realize that is far from what you are doing, butdo you think it would be feasible for us to try and adapt your code?

One thing that strikes my ear in the samples, most obviously in the street noise one, is that the algorithm is acting more like a gate than noise removal since the horns and traffic are clearly audible still in the speech sections.

I would love to see this adapted to guitar noise suppression!

Thanks for this work.

Re: Fab!

Date: 2017-10-02 12:30 am (UTC)
From: (Anonymous)
Lol, plz excuse autocorrect. I have no idea what "department" was supposed to be!

What about reducing noise in music?

Date: 2017-10-02 03:56 pm (UTC)
From: (Anonymous)
Do you think this approach would be helpful in reducing noise on old 78 rpm records? Because's Great 78 project has a large number of albums you could experiment with.

Re: What about reducing noise in music?

Date: 2017-10-06 03:47 pm (UTC)
From: (Anonymous)
Hmm, perhaps you can store the RNN's coefficients in a file so one can train the filter to match his needs?

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)…


Date: 2017-10-09 08:21 am (UTC)
From: (Anonymous)
Hi Jean Marc,
According to the audio samples you provided, it seems that the RNNoise has more residual noise than the speex. Do you think it will perform better with more noise samples for training?


jmvalin: (Default)

September 2017

2425 2627282930

Most Popular Tags

Page Summary

Style Credit

Expand Cut Tags

No cut tags
Page generated Oct. 19th, 2017 11:46 pm
Powered by Dreamwidth Studios