![]() ![]() The signals we sampled were in hundreds of MHz but the concepts are exactly the same as in audio frequencies. Not entirely related to your question on audio aliasing, but many moons ago I was an EE on a team using electro-optics to design a much better Analog-to Digital converter than you could buy commercially. ![]() In other words, what are those pathological cases? This video is a really good start and clears up a lot, but in some ways it only scratches the surface. But I am interested in really understanding what information is lost, what isn't, and why that may or may not make any difference. Generally the noise floor in the original recording is too high to tell the difference, even if you could hear a difference in artificially constructed pathological cases. Is is possible to show that such information either isn't lost, or that what is lost is either below the noise floor or outside the audible frequency range? This particular one is a fairly common complaint made by audiophiles about 44KHz/Nyquist, so it would be nice to see it addressed head-on.įWIW, I'm a bit of an audiophile myself, but not one of those people who thinks they can hear a difference between 192kbps MP3 and uncompressed, let alone 16/44 vs 24/192. Although we can't hear sounds (much) above 20KHz, we can detect artefacts such as "beats" produced when harmonics are slightly mismatched. It definitely exists and I have a very good understanding of aliasing with respect to computer graphics, but what effect does aliasing really have on an audio signal? In CG it's something that's talked about all the time and there are tons of papers about it, but it seems (from the outside at least) that audio guys only ever talk about aliasing in very hand-wavy terms. Following on from that: the effects of aliasing. What happens when you start combining waves together to create more complex signals? This is pretty important, since any real instrument produces hundreds of harmonics with complex attack and decay properties, so decomposition of the fundamental frequencies won't be as accurate as with toy examples. I really hope there's a follow-up at some point, because as someone with a decent amount of experience with computer graphics, but only a fairly general understanding of sampling theorem, the stuff that would naturally come next is what most interests me, namely: ![]()
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