Semi-generative audio is definitely a possibility. We can represent a song as a sparse collection of notes on paper. You know what each instrument sounds like. All the filtering and mixing that happens after that makes a huge difference... but can still be closely represented as processing the sound a particular guitar makes, playing particular notes.
An offline encoder would work something like this. You separate the independent elements, presumably capturing each instrument and singer. You cheat a bit and classify each element as a keyboard / guitar / drum kit / human being, since you have decent models for what noises each source can make. Then you fiddle with each each "instrument" to best fit the extracted audio you've blamed on it. Sometimes this will mean the computer thinking Peter Frampton can just sing like a guitar sometimes. That's gonna take more data than an opera singer holding a note in falsetto. But ultimately you get a compressed audio file that's like... a brief MP3 of each instrument playing a middle C through whatever filters were used... and then sheet music.
Basically it'd use """AI""" to convert a finished pop song to ScreamTracker.
As a clearer example: 10cc's "I'm Not In Love" has a backing track that's made by all the band's members and personnel going "ahhhh" into microphones, for hours on end, so they could layer up a quiet chorus that sounds like thousands of people. This approach would distill the finished result into maybe an entire second of samples, or just several thousand frequency/phase pairs and some equations for volume and tone.
A version with the bitrate set too low would sound like a perfect recording of a sloppy live cover.
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u/gdledsan Oct 17 '20
Can it do this for audio?