The user can paste a youtube url which will then be analysed, fingerprinted and matched against a database of 7+ million audio fingerprints. It does not only identify a single song but is able to identify multiple songs contained in a single file or video and generates a timeline listing which tracks it contains at which time. Our matching algorithm is based on the open source echoprint-codegen fingerprinting method,…
I've worked on the echoprint-codegen algorithm for my current project ( trak.rocks ) and I'm curious about how you reimplemented the matching logic ? Do you plan to document/opensource you work ?
Also we pack each individual hash before storing in Elasticsearch and gained at least 50% storage space this way.
Our Fingerprint data is quite different from theirs(unreliable ID3 tags, N versions of same track) which is why we needed some tweaks. So far the matching is still far from perfect...
Whether we will open source the whole thing at some point we don't know yet.