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

Input and output data dimensions

Date: 2018-11-08 01:20 pm (UTC)
From: (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

Date: 2018-11-08 04:33 pm (UTC)
From: (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/, 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?


jmvalin: (Default)

November 2018

1819 2021222324

Most Popular Tags

Page Summary

Style Credit

Expand Cut Tags

No cut tags
Page generated Feb. 19th, 2019 05:08 pm
Powered by Dreamwidth Studios