(my blog has moved this is just a copy of this post)

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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Over the last three years, we have published a number of Daala technology demos. With pieces of Daala being contributed to the Alliance for Open Media's AV1 video codec, now seems like a good time to go back over the demos and see what worked, what didn't, and what changed compared to the description we made in the demos.

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Here's the latest addition to the Daala demo series. This demo describes the new Daala deringing filter that replaces a previous attempt with a less complex algorithm that performs much better. Those who like the know all the math details can also check out the full paper.

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Here's my new contribution to the Daala demo effort. Perceptual Vector Quantization has been one of the core ideas in Daala, so it was time for me to explain how it works. The details involve lots of maths, but hopefully this demo will make the general idea clear enough. I promise that the equations in the top banner are the only ones you will see!

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As a contribution to Monty's Daala demo effort, I decided to demonstrate a technique I've recently been developing for Daala: image painting. The idea is to represent images as directions and 1-D patterns.

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Three years ago Opus got rated higher than HE-AAC and Vorbis in a 64 kb/s listening test. Now, the results of the recent 96 kb/s listening test are in and Opus got the best ratings, ahead of AAC-LC and Vorbis. Also interesting, Opus at 96 kb/s sounded better than MP3 at 128 kb/s.

• new analysis code and tuning that significantly improves encoding quality, especially for variable-bitrate (VBR),


• automatic detection of speech or music to decide which encoding mode to use,


• surround with good quality at 128 kbps for 5.1 and usable down to 48 kbps, and


• speed improvements on all architectures, especially ARM, where decoding uses around 40% less CPU and encoding uses around 30% less CPU.