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Can you precisely define what you mean by "generating" and "pretends", in such a way that this neural network does both these things, but a conventional modern
by shwaj 3y ago
Can you precisely define what you mean by "generating" and "pretends", in such a way that this neural network does both these things, but a conventional modern audio codec doesn't?
"Pretends" is a problematic choice of words, because it anthropomorphizes the algorithm. It would be more accurate and less misleading to replace "pretends to be" with "approximates". But then it wouldn't serve your goal of (seeming to) establish a categorical difference between this approach and "regular algorithms", because that's what a regular algorithm does too.
I apologize, because the above might sound rude. It's not intended to be.
- thomastjeffery 3y agoI was avoiding the word "approximate", because that implies a connection to the original raw data. A generative model guesses what data should be filled in, based on what is present in its own model. This process is totally ignorant of the original (missing) data. To contrast, a lossy codec works directly with the original data. It chooses what to throw out based on what the algorithm itself can best reproduce during playback. This is why you should never transcode from one lossy codec to another: the holes will no longer line up with the algorithm's hole-filling expectations.