It fits into that space of an unusual but comprehensible problem, unlike superficially similar features like recognizing animals or objects in images, which is mostly weird ML magic.
You're dead on that it's pretty difficult if you don't benefit from others, we did a ton of work that in retrospect wasn't necessary. I liked the advanced psychoacoustic model, faithfully implemented in high performant C direct from Zwicker. (Psychoacoustics). To a first approximation, about 10/s model -> pca -> top 16 dim -> VQ and the resulting bytes contain more than 50% of the entropy (!!) Shove all of those in a home grown what-you-now-call-a vector DB, do dozens of range queries, and search for any song common to multiple results. Boom, music recognition. Understandable in retrospect but things like that aren't Everest they're like... multiple unclimbed mountains.
0. And far too early to have any applications. Company existed 2000-2001 \o/
https://patents.google.com/patent/US7853664B1/
https://patents.google.com/patent/US6941275
Very interesting hearing about all of the differing approaches people have taken to solving this problem! Do you have further writings on this topic?
Rather, matching two recordings of the exact same performance (one ingested by Shazam at training time and one ingested by Shazam at run time) is more akin to identifying individuals (facial recognition) than identifying species.