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Yes, deep neural networks have proven remarkably useful for machine perception, but you would still need to collect a colossal amount of audio data, fingerprint all of it, build a low-latency processing infrastructure for making inferences, and convince a hundred million people to install your software to feed you copious real-world training data that you can use to improve model performance.
That's actually the easy part. You already have the music. Distorting it by superimposing background noise is really not difficult.
I think you just confirmed how easy (and cheap) it is to actually generate this data.
I mean, you can easily find thousands of hours of music online. Recording background noise is easy (just go to a random bar where they are not playing music). Now simply add the two signals (you can shift them randomly to generate more data). You can also add some linear filtering if you like (just imagine random settings of an equalizer for starters).
This should give you enough data to build a proof of concept at least.
Illegally grabbing thousands of hours of music to train a commercial model hardly qualifies as fair use. Any company you build upon that would be tainted.
For sustaining:
In addition, you'll need to keep an updated catalog of music to identify new songs against, and most uses of a service like shazam are to find names of songs people aren't familiar with, so that catalog needs to be very fresh.
That means you'll have to grab some sort of feed, and engage in large scale music piracy for commercial gain or have access to a library of songs from many disparate music providers, such as ascap.
Background noise:
there are literally hundreds of different background noise environments you need to train against. Dozens of common microphone configurations. Clipping, variations.
It's very much a problem where a proof of concept is neat but doesn't really get you anywhere.
Shazam doesn't actually let you improve the answer, nor report incorrect guess. They are so confident with them, even if it's sometimes completely missed genre and style of music.
Also, it works a lot better than being able to find "slightly distorted" versions. It can catch a song in a noisy room where you can barely make out the song to begin with. Couple months back it found a song when there was a very loud crowd yelling over it. They're also able to determine differences between versions of songs pretty well. Some remixes might sound very close to the original.
Other thing you might be missing is just how fast it is even on a slow mobile connection.
Read this article, then come back: [1]
I think describing the reaction as lack-of-appreciation is a bit misleading. Perhaps disbelief might be a better description.
The smart money, though, is on the main chance: you don't understand the purchase, or the problem domain, or both.
In this case I think you are overestimating the progress in NN and search, and underestimating the signal processing. Have you tried this with any significant corpus?
"Whack it through a FFT and do correlation " seems like one of the obvious solution to the toy problem version, but this is exactly the sort of thing that usually falls apart in practice.
Is anyone keeping a running list of products that HN commenters have suggested could be built in an afternoon/weekend?
Ones I've seen so far: Facebook, Twitter, Dropbox, and now Shazaam.
Then we'll talk.
https://www.toptal.com/algorithms/shazam-it-music-processing...
What's not straightforward is recognizing cover songs and the like. But that's not only non-trivial but AFAIK can't be done.
Well, you could translate the music into actual notes (or musical intervals), and use Smith-Waterman (or any more advanced and more recent technique) to find the song with the lowest edit-distance.
https://www.toptal.com/algorithms/shazam-it-music-processing...
Also, even if you have many false positives, you have already narrowed down the search, and this allows you to do more brute-force searching like computing cross-correlations.
I'm not disputing they overpaid. However, long to short, building the technology is the easy part, and just a fraction of the brand / product value.
"What's that song?" is a different signal to buying a song. Especially when "what's that song?" isn't restricted by licensing agreements.