Basics of MusicIP’s MusicDNS and MusicAnalysis

Deriving from musicbrainz the system MusicIP created finding similar music works, in short, in three steps:

  1. analyse the music audio signal (up to 10 min of a track) locally by MusicIP Mixer generating an id called PUID (closed source!)
  2. PUID is sent to MusicDNS, a web-service by MusicIP (closed source, too!) which does fuzzy matching
  3. Some magic happens that the Mixer calculates a playlist by. It would not be sufficient for the DNS (Music Digital Naming Service, don’t mistaken it with Domain Name System) server to just return a list of PUIDs since the server (hopefully!) doesn’t know about all other tracks I have in my library, i.e. that potentially could be used to generate playlists with.

PUIDs

PUID is a 128-bit Portable Unique IDentifier that represents the analysis result from MusicIP Mixer and therefore is not a music piece finger print identifying a song in some particular version. PUIDs are just the ids used in the proprietary fingerprinting system operated by MusicIP. They provide a lightweight PUID generator called genpuid that does 1. and 2. PUIDs can be used to map track information such as artist, title, etc. to a finger print. The id itself has no acoustic information.

Acoustic Fingerprinting

Refering, again, to musicbrainz’s wiki acoustic fingerprinting here is a different process using only 2 minutes of a track. This fingerprint than is send to a MusicDNS server which in turn matches it against stored fingerprints. If a close enough match is made a PUID is returned which unambiguously identifies the matching fingerprint (Also see a list of fingerprinting systems. There is also an scientific review of algorithms). This is necessary since source to generate PUIDs or submit new ones is closed source.

On the other hand wikipedia defines acoustic fingerprinting as follows:

An acoustic fingerprint is a unique code generated from an audio waveform. Depending upon the particular algorithm, acoustic fingerprints can be used to automatically categorize or identify an audio sample.

This definition is even quoted by MusicIP’s Open Fingerprint™ Architecture Whitepaper (page 3).

MusicDNS

The web-service mainly is to match a PUID to a given acoustic fingerprint and look up track metadata such as artist, title, album, year, etc. (aka tags) as done by the fingerprinting client library libofa which has been developed by Predixis Corporation (now MusicIP) during 2000-2005. Only the query code is public via the MusicDNS SDK; music analysis and PUID submitting routines are closed source!

Getting the Playlist

Up to now I couldn’t figure out or find sources how this is actually done by Music Mixer. I’ll keep you posted as I find out.

Other sources / Directions

2 Comments

  1. NorskeDiv said,

    Wednesday, 21st Apr 2010 at 10:34

    Too bad MusicIP either went under or was bought out. I don’t know what the circumstances were for the previous owners, I hope they did well from it. It’s a great technology, and they were the first ones out there auto generate playlists based on the “mood” of a song. Now Apple has copied them with Genius.

    MusicIP is still available:
    http://www.amplifindmusicservices.com/what/downloads.php

    There are some piddling shortcomings and I wish I had the code, I could probably fix them in a day. So that is annoying. The software however is still quite usable and I highly recommend. If you have a huge music collection and are interested in auto-generating playlists, its not to be beat. I actually found Genius on iTunes to be lacking compared to MusicIP Mixer.

    Do you know of any other competing products out there? I mean, ones which do acoustic analysis. Last.fm often finds interesting music, but it’s acoustically different and therefore most often a poor playlist. To my knowledge, there’s only Genius, and MusicIP Mixer (no longer updated). 😦

    There was something for some KDE Music player that I came across… can’t remember the name though as I don’t run Linux.

  2. tovorinok said,

    Thursday, 5th Jul 2007 at 14:11

    Hello

    Great book. I just want to say what a fantastic thing you are doing! Good luck!

    Bye


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