This new demo presents LPCNet, an architecture that combines signal processing and deep learning to improve the efficiency of neural speech synthesis. Neural speech synthesis models like WaveNet have recently demonstrated impressive speech synthesis quality. Unfortunately, their computational complexity has made them hard to use in real-time, especially on phones. As was the case in the RNNoise project, one solution is to use a combination of deep learning and digital signal processing (DSP) techniques. This demo explains the motivations for LPCNet, shows what it can achieve, and explores its possible applications.
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!).
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.
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!
We just released Opus 1.1-beta, which includes many improvements over the 1.0.x branch. For this release, Monty made a nice demo page showing off most of the new features. In other news, the AES has accepted my paper on the CELT part of Opus, as well as a paper proposal from Koen Vos on the SILK part.
I just got back from linux.conf.au 2012 in Ballarat. The video for the talk I gave, Opus, the Swiss Army Knife of Audio Codecs, is now available on the Opus presentations page. For the Ogg-impaired, a lower-quality version is also available on YouTube.
For those who are into speech codecs, I also recommend watching David Rowe's presentation: Codec 2 - Open Source Speech Coding at 2400 bit/s and Below. His presentation was selected as one of the four best talks at LCA this year -- well worth watching.
Those who have been following the Opus git repository in the past few weeks probably haven't noticed much work going on. The reason is pretty simple, most of the work has been going on elsewhere in an experimental branch (exp_wip3 names for now) of my private repository. The reason it's in an experimental branch is that its not fully converted to fixed-point and hasn't been tested on any frame size other than 20 ms. Here's an (incomplete) list of changes for now:
- Really unconstrained VBR (not trying to keep the same average rate)
- Tonality detection to give highly tonal audio a boost in bit-rate
- (yet another) rewrite of the transient detection code
- New dynamic allocation code that boosts the rate of bands that have significant spectral leakage caused by short blocks
Thanks to these changes, the quality has (as far as we can tell) gone up compared to the current master branch. I invite you to judge for yourself by comparing the audio coded with the current master branch with the audio coded with the new exp_wip3 experimental branch. This is 64 kb/s, so fairly low rate for stereo music. The original is here. Let me know what you think.