Surprised nobody has mentioned MusicBrainz, it's the free and open source music fingerprinting database which powers the Picard, Jaikoz, Beets, etc taggers. They have been doing audio fingerprinting for years, you can download the DB or access it via a web API. The author's solution may work quite well with small number of entries to match against, but I suspect the match rate goes down significantly when lookup is against hundreds of thousands or millions of other fingerprints.
Audio fingerprinting as used by MusicBrainz is a little different concept. Because it doesn't have the need to match short phone-recorded samples, we can use more efficient algorithms for both the fingerprinting and their matching. It's usually not the match rate that goes down when dealing with a large database, but the false match rate that goes up. And of course performance. Those were my two main things to worry about when I was working on AcoustID (the current fingerprinting technology used by MusicBrainz).
https://wiki.musicbrainz.org/Fingerprinting