this post was submitted on 29 Sep 2023
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With a neural network, you wouldn't be able to mathematically prove that the signal is perfectly recovered 100% of the time for all possible inputs. That is the case with PNG and FLAC. If you're just listening to music and need a good compression ratio, then sure, it won't be a big deal if a couple of bits are wrong. But that's also why we have lossy compression. If the goal is to make signal degradation imperceptible to a human, then you could get a much better compression ratio using neural networks. If it's truly critical that the signal isn't corrupted, it would probably be better to just use the original method.
That isn't really the case; while many neural network implementations make nondeterministic optimizations, floating point arithmetic is in principle entirely deterministic, and it isn't too hard to get a neural network to run deterministically if needed. They are perfectly applicable for lossless compression, which is what is done in this article.