cochlear

How funny, I actually corresponded with one of the authors of the "Spiking Music..." paper when it first showed up on arxiv. I'll definitely give the amp-modeling paper a read, looks to be right up my alley!

Now that I understand the basics of how this works, I'd like to use a (much) more efficient version of the simulation as an infinite-dataset generator and try to learn a neural operator, or NERF like model that, given a spring mesh configuration, a sparse control signal, and a time, can produce an approximation of the simulation in a parallel and sample-rate-independent manner. This also (maybe) opens the door to spatial audio, such that you could approximate sound-pressure levels at a particular point in time _and_ space. At this point, I'm just dreaming out-loud a bit.

Thanks for the bug report, I definitely do see now that things fall apart at certain extreme values. I'll look into it and see if I can fix. My assumption is that it's some sort of numeric overflow or underflow resulting in NaN values, and that I'll probably just need to set the slider boundaries more carefully. Thanks again!

These are both great points and I'll use them to refine my writing on the subject, I appreciate the feedback!

Apologies if it isn't clear, but the animated gifs are meant as an illustration of the iterative encoding process, where the encoder decomposes the signal step-by-step, as in matching pursuit. I'll be sure to clarify that point.

I'll add a paragraph on compression rates/ratios, although that isn't necessarily the main focus here; codecs may compress a signal, but they might also transform it into a more useful, easy-to-understand and easy-to-manipulate representation.