Re: oversampling?
Posted: Wed Dec 20, 2023 7:51 pm
Grant, isn't that just a very complicated way of looking at L?
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i don't really get you're point here. why wouldn't it be a paper just because it's from a student? it's not peer reviewed and wasn't published in a scientific magazine, sure. but it is well researched and the information is still valid. it's old, but that doesn't make it any less correct. the math didn't magically change and the experiments done in the paper won't have different results if you replicate them today.
so that means if i turn a lowpass filter all the way down or all the way up it's not a filter anymore? what is it then?ColinP wrote: ↑Wed Dec 20, 2023 6:29 pm I'm struggling with some of your comments. I'm reasonably confident that fractional buffer access using lerping can produce non-bandlimited signals as I think I've shown this to be the case from both a theoretical and experimental POV. I've also addressed why it's pretty daft to view this through the lens of low-pass filtering given that when f = 0 or f = 1 it certainly isn't and in most other circumstances f jumps about all over the place so the frequency response alters sample by sample. OK one could argue that it's filtering Jim, but not as we know it...
It's a way of calculating (and visualising) a long series of 'L's without resorting to spreadsheets or anything. It took me longer to screenshot and describe it than it took to execute in Audacity.
Right so I understand that English isn't this guy's first language but I think he's claiming to have not slept for three weeks. Now in my misspent youth I confess that I sometimes did three solid day coding sessions (72 hours work with no sleep) with chemical assistance, but three weeks is pushing it.Welcome to read the paper that took three entire weeks (24/7) of my life, approximately 1/1000 of the whole deal.
This isn't exactly precise analysis shall we say.The presented optimal interpolators make it possible to do transparent-quality resampling for even the most demanding applications with only 2x or 4x oversampling before the interpolation. However, in most cases simple linear interpolation combined with a very high-ratio oversampling (perhaps 512x) is the optimal tradeoff. The computational costs depend on the platform and the oversampling implementation.
Therefore, which interpolator is the best is not concluded here. You must first decide what quality you need (for example around 90dB modified SNR for a transparency of 16 bits) and then see what alternatives the table given in the summary has to suggest for the oversampling ratios you can afford.
what's your point here? are you mocking their english? or that they had a little fun on the first page of whatever this text is. english is not my native language either. maybe that's why i am not so anal about it. moreover, that's just a short fun introduction giving acknowledgements and a little insight into how this text came to be. why does that matter? how is this related to the provided information? does this first page text really disqualify the whole body of work and all of the research that follows for you?ColinP wrote: ↑Fri Dec 22, 2023 5:45 pm
It begins with ...
Right so I understand that English isn't this guy's first language but I think he's claiming to have not slept for three weeks. Now in my misspent youth I confess that I sometimes did three solid day coding sessions (72 hours work with no sleep) with chemical assistance, but three weeks is pushing it.Welcome to read the paper that took three entire weeks (24/7) of my life, approximately 1/1000 of the whole deal.
I'm no mathematician but I know that 3 weeks times 1,000 is 3,000 weeks and according to my calculator that would mean he is about 57 years old.
There's a photo of him on his website dated 2021.
http://yehar.com/blog/
I wish I looked that good when I was 55.
why does it need to be precise? that's a valid conclusion. "here's a bunch of measurements and results. there is no single best method. decide on one in the context of your use-case with the help of the data given in this text." that sounds very reasonable to me. also we have completely left the original point anyways. this person has done research and compiled data that shows how interpolation (including linear interpolation) has an effect on the frequency spectrum of signals. mainly gentle lowpassing. this sole image was the essential part: look, i am sorry that you had bad experiences in the scientific world. and yes, i may have mislabeled the text as a "paper", because it was called a paper in the text itself. but neither of those things make the research, experiments and measurements any less valid. if you don't trust this person, go ahead and repeat the test setup. after all that's what scientific work is all about, isn't it?ColinP wrote: ↑Fri Dec 22, 2023 5:45 pm Then the conclusion ...
This isn't exactly precise analysis shall we say.The presented optimal interpolators make it possible to do transparent-quality resampling for even the most demanding applications with only 2x or 4x oversampling before the interpolation. However, in most cases simple linear interpolation combined with a very high-ratio oversampling (perhaps 512x) is the optimal tradeoff. The computational costs depend on the platform and the oversampling implementation.
Therefore, which interpolator is the best is not concluded here. You must first decide what quality you need (for example around 90dB modified SNR for a transparency of 16 bits) and then see what alternatives the table given in the summary has to suggest for the oversampling ratios you can afford.
I can think of at least one case where this is appropriate: Implementing 'waveguides' for physical modelling techniques. In the Zeit Bundle, there is a 1V / Oct Pitch to Delay Time Converter module. The delay time for a particular resonant frequency rarely falls on an exact, integer number of sample periods. This becomes even more apparent when you get up to higher pitches. If you didn't have the ability to specify fractional delays, your pitches would be quantised to integer divisors of the 48 kHz sample period. The quantisation effect gets much more pronounced as the pitch increases.
[Emphasis mine, above] I think this is the crux of the matter:What's going on here is nothing more complex than lerped fractional buffer access at a fixed rate and it produces aliasing.