I am somewhat of a SpectraLayers novice, but have been amazed by some of the things I have been able to do with it.
One of my favorite experiences was with a long crappy shotgun recording of a baby sea otter. The otter was quite far away, and there was a ton of background noise: cars, boats, bells, chattering people, dogs, seals, machinery and other wharf noises. Nine minutes of loud boring din with just a few seconds of otter (baby otters, alas, do not perform on request).
The baby otter had a keening cry, with lots of upper harmonics. By fiddling with the amp/res/FFT parameters, SpectraLayers made it easy to visualize and highlight the otter cries, and spot every one of them distinctly in the nine-minute long recording.
Using the wand/harmonic selection tools, it was then a fairly quick process to separate and layer one of the cries, so that one layer had all the background noise with no detectable trace of the otter, and one layer contained a pristine (if distant) otter cry. With the manual tools, the separation was quite amazing.
One abstract result of the layered separation was a clear visual sense of a sonic signature: what a baby otter cry “looks like” and what gets left behind as background.
Is there any way to have SpectraLayers “learn” a sound signature from one or more manual separations, and then automate the process of finding and separating the rest of them in an audio file? (It would be a bit like the built-in Unmix tools, except based on very specific learned examples and presumably more precise: a kind of fuzzy logic find-and-separate.)
Baby otter cry: