Opus Audio Codec on YouTube
This article explains what the Opus audio codec is and how YouTube uses it.
What is the Opus Audio Codec?
How does YouTube use the Opus audio codec?
You want the best possible, distortion-free playback of your song on all platforms?
What bitrates does the Opus audio codec use?
Can the use of the Opus audio codec cause distortion?
How do I avoid codec distortions?
You want to know if your song works on all platforms? We'll tell you in our professional mix analysis.
Opus Codec gets AI update
The new version 1.5 or 1.5.1 of the license-free audio codec Opus has received an AI update. Machine learning (ML) is intended to improve the coding so that the data stream remains compatible with existing decoders. But the decoder also receives artificial intelligence to improve the sound.
AI for better sound quality
A technology called "Neural Vocoder" is designed to compress speech particularly efficiently. Compared to the LPCNet vocoder, the CPU cores of laptops or smartphones are only loaded by about one percent. The developers call the algorithm Framewise AutoRegressive Generative Adversarial Network (FARGAN). They plan to publish a paper on this later.
They optimize signal processing with the Linear Adaptive Coding Enhancer (LACE) and a nonlinear variant (NoLACE). LACE behaves like a classic postfilter, in which a deep neural network (DNN) adjusts the coefficients on the fly with all available data - but the audio signal itself does not pass through the DNN. The result is a small DNN with very low complexity that also works on older phones. The NoLACE variant requires more computing power, but is also significantly more powerful due to the non-linear signal processing. Both significantly improve the voice quality.
The Opus codec noticeably shapes YouTube sound — how you prepare your mastering for it is explained in the article.
Mastering Guide
into the streaming context.
Opus codec: Bitstream remains standards compatible
Instead of programming a completely new codec based on ML, Opus remains completely compatible. This ensures that Opus continues to run on older and slower devices while providing an easy upgrade path. While deep learning is often associated with powerful GPU accelerators, the Opus project has optimized everything so that it runs on most processors, including smartphone CPUs.
Most users shouldn't notice the higher load, but those using microprocessors or smartphones that are more than five years old might notice it. The new functions are therefore still deactivated by default and must be activated during compilation and at runtime, for example via command line parameters.
Improve packet loss
Packet loss leads to missing sound fragments. Codecs usually try to prevent this through packet loss concealment (PLC). This is usually a kind of decoder-side interpolation with "plausible audio" inserted at the loss points. Machine learning could be particularly helpful here - the Opus developers are tackling this with a deep neural network (DNN), which increases the codec's binary file by around 1 MByte and leads to one percent more load on a laptop CPU core in the event of severe packet loss.
Tip:
Listen to how your master sounds after using the YouTube Opus codec.
– free in the browser with codec preview in the loudness meter.


