[MontagFTB]

> We do [cubic curve fitting] all the time in image processing, and it works very well. It would probably work well for audio as well, although it's not used -- not in the same form, anyway -- in these applications.

Is there a reason the solution that "works very well" for images isn't/can't be applied to audio?

[adgjlsfhk1]

The short answer is that our eyes and ears use very different processing mechanisms. Our eyes sense using rods and cones where the distribution of them reflects a spatial distribution of the image. Our ears instead work by performing an analogue forier transform and hearing the frequencies. If you take an image and add lots of very high frequency noise, the result will be almost indistinguishable, but if you do the same for audio it will sound like a complete mess.

[amlib]

AFAIK it introduces harmonic distortion

[pixelpoet]

I'd love to know more about this, do you perhaps have any refs? Thanks

[amlib]

Not an expert in this field, just a scrub, so I can't really give you much.

There is this website that has painstakingly compares many resampling algorithms from all sorts of software:

https://src.infinitewave.ca

Try it's mirror if you can't access it: https://megapro17.github.io/src/index.html

The only one that says it is a cubic interpolation is the "Renoise 2.8.0 (cubic)" one, the spectrogram isn't very promising with all sorts of noise, intermodulation and aliasing issues. And, by switching to the 1khz tone spectrum view you can see some harmonics creeping up.

When I used to mess with trackers I would sometimes chose different interpolations and bicubic definitely still colored the sound, with sometimes enjoyable results. Obviously you don't want that as a general resampler...

[butterknife]

Just to note that this site hasn't been updated for a while.

Much better, more modern and with automated upload analysis site would be [1] although it is designed for finding the highest fidelity resampler rather than AB comparisons.

[1] https://src.hydrogenaudio.org