[rhizome]

You can certainly automate crossfaded audio between multiple sources to try to get the cleanest copy, but it's hard. For instance, how do you decide whether it's noise or the letter "s" or the "chk" of a pick across muted guitar strings? The heuristics for "better than any constitutent audio source" can be extremely nuanced, algorithmically intensive, and still difficult to pin down, akin to speech recognition. Speaking purely to SaaS'y automated purposes, natch.

Typically what it seems you're talking about for audio here is similar to a matrix mix in the amateur/live audio world. People have been (manually) mixing soundboard audio with audience-recorded audio to improve the audio quality of recorded shows for some years now.

[anjc]

I don't know anything about audio processing algorithms, but (assuming there's a way), presumably with enough audio sources, there'd be a commonality between each one that describes the 'correct' sound. I.e. if there's different noise going on in each source (people talking around each microphone, at a gig, intermittently), you don't really need to decide which sound is 'clean' because you'd know which sounds are inconsistent...(?)

[rhizome]

Sure, but it's determining what is "correct" that is the hard part. You could use a majority-rule if you have three or more sources, but the more additional sources are required starts getting into pretty niche territory and it still remains possible that the minority source is the most faithful one.