Music analysis software [closed] - machine-learning

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Greetings
I may have imagined this but does anyone know if Last.fm previously used some form of open source project to perform analysis on music to determine similar music.
As its now moved to a pay version I'd like to make something which can add known music to my playlist. (I hate scanning my computer for similar music manually)
Failing that - does anyone know of any system that I could use to replace this ? Ideally I'd like some form of API / Source code that I can use to automate the whole process into batch jobs.
Thanks,
[edit]
Ideally I was looking for something more along the lines of content matching. I'm the type of person who just throws all my music into one unorganized location. Then being lazy I would ideally expect a playlist to be generated giving me a similar music type of playlist.
Last.fm uses http://www.audioscrobbler.net/ - it also provides access to its database via an API.
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Answer 1

Music similarity is not an easy problem.
There are two general approaches to solving this problem.
Approach 1.
Throw data at the problem. This is the approach LastFM and Pandora take. It's basically one huge database which is maintained by either a community or group of experts. Note that to use this approach you will need clean metadata or some kind of audio fingerprinting solution like musicbrainz. Once you have the feature database you can use algorithms such as Pearson correlation coefficient to find similar items.
Approach 2.
Throw algorithms at the problem. In particular, computer audition algorithms. This means you calculate vectors of various features a song contains and using neural nets and a variety of other techniques you find other songs with similar vectors. This approach has been used successfully for automatic genre classification and query by example.
If you are looking for open source software for music analysis, marsyas can do pretty much everything the commercial stuff can do. Its the brain child of George Tzanetakis and on his web site you can find many papers about the state of affairs with computer audition.

Answer 2

There's a web API at The Echo Nest that includes a get_similar web service that allows you to retrieve similar artists to a set of seed artists. You can use this to help build playlists. The Echo Nest also has a set of web APIs that will perform a detailed analysis of a track (similar to the aforementioned Marsyas) that one could use as the basis for an acoustic-based song similarity method. (Caveat, I work at the Echo Nest). Of course, if you use iTunes, there's some canned solutions. iTunes now has a music recommender / playlist generator that will build playlists of songs from simliar artists. Similarly, the company Mufin has an iTunes add on which will perform acoustic analysis of your tracks and use this analysis to build playlists.
If you are interested in building your own music similarity system, I suggest that you take a look at the proceedings for ISMIR (the International Society of Music Information Retrieval). There's quite a bit of research around music similarity and playlisting that you'll find helpful. You can find the proceedings at ismir.net

Answer 3

Wouldn't it be simpler/more efficient to query(build?) some internet database based on genre/style/etc? I used last.fm and similar sites but never felt they did anything more then this (at least the results weren't indicating that) ;)

Answer 4

I am not very sure what exactly you want, but how about MusicBrainz?

Answer 5

To be clear, AudioScrobbler is the tech built by Last.fm to run their service. They collect stats on the tracks which people listen to (also 'Like's of tracks and artists).
So Last.fm does social similarity... users who listened to X also listened to Y - you like X so maybe you will also like Y.
Given a large enough user base submitting stats, social similarity is likely to provide better results than computer analysis approaches. For example, try querying the Last.fm API for similar artists to someone you know - probably comes up with some good matches and a few obscure or oddball ones, which nonetheless reflect real people's listening habits. The more obscure the artist you search for the more likely you'll get weird matches.
Even if you could get the automatic genre classification method described by George Tzanetakis to work well you are missing out on the subjective judgements of quality supplied by real people. eg two tracks both look like 'Jazz' but there are many different kinds of Jazz... and I might be interested in non-Jazz albums that a favourite jazz musician has played on. Social similarity would be more likely to capture that info.

Answer 6

I used to use Predixis Magic Mixer. It will perform a brief analysis of the audio in a file, produce a "finger print" and compared it to fingerprints in a central database. If listed, it would set an identification code which is the result of the analysis of the entire file into the client copy. If not, it would do a full analysis on the client computer (takes a while) and upload that to the central database and keep the local copy as well. From that information it can set up a play list that relates tunes, one to another' depending upon the actual sounds. I have not used it for a few years so I don't know if the central database servers still are in operation, but a web search says no. It should still work, but every file will require full analysis.