As a matter of fact, I've converted the frequency representation that the core algo extracts back to audio without noticeable quality loss. Although in the app I am just showing the amplitude, but the phases are also important (people usually say that the phase of the note is not important for perception, but actually in complex sounds it's important to preserve relative phases of different frequencies).

I think this intermediate representation that I've extracted using this algo would be much better for machine learning on the sound data than either 1) raw sound or 2) frequencies extracted with FFT. But there is an engineering difficulty to overcome: the frequency data that the algo extracts doubles in its amount (more frequent frequency samples) with each octave... It's like wavelet data... It's a challenge to feed this data to standard ML algorithms, need to think of how to configure the inputs, it would have to be highly hierarchical.

My initial goal was to "learn" different instruments from raw sound data, and this intermediate representation is good, because it allows "translation invariance" across frequencies. I've described it here: https://vsound.app/high-precision-in-frequency-domain.html

It's a work in progress... This app is just for me to see if people are generally interested in this area, I don't want to do something for months only to discover nobody wants it (although it's been a few months I've worked on this app LOL).