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"The key to lossy compression is to identify changes that will not be perceivable by humans, as perfect reconstruction is impossible at low bit rates. To do so, we use discriminators to improve the perceptual quality of the generated samples. This creates a cat-and-mouse game where the discriminator’s job is to differentiate between real samples and reconstructed samples. The compression model attempts to generate samples to fool the discriminators by pushing the reconstructed samples to be more perceptually similar to the original samples."

It's worth noting that using a neural network for audio compression and decompression is far from new—especially for speech compression—but Meta's researchers claim they are the first group to apply the technology to 48 kHz stereo audio (slightly better than CD's 44.1 kHz sampling rate), which is typical for music files distributed on the Internet.

As for applications, Meta says this AI-powered "hypercompression of audio" could support "faster, better-quality calls" in bad network conditions. And, of course, being Meta, the researchers also mention EnCodec's metaverse implications, saying the technology could eventually deliver "rich metaverse experiences without requiring major bandwidth improvements."

Beyond that, maybe we'll also get really small music audio files out of it someday. For now, Meta's new tech remains in the research phase, but it points toward a future where high-quality audio can use less bandwidth, which would be great news for mobile broadband providers with overburdened networks from streaming media.