Hi all,

I'm looking for some guidance about which techniques/algorithms I should research for the following problem. I've currently got an algorithm that clusters extremely similar-sounding mp3s using acoustic fingerprinting. In each cluster, I have some metadata (song/artist/album) for each file. For that cluster, I'd like to pick the most representative song/artist/album that matches an existing row in my database, or if there is no best match, decide to insert a new row.

For a cluster, there is generally some correct metadata, but individual files have many types of problems: - Artist/songs are completely misnamed, or just slightly mispelled - the artist, song, or album is missing, but the rest of the information is there - the song is actually a live recording, but only some of the files in the cluster are labeled as such. In this case the song name should have (live) in it.
- there may be very little metadata, in some cases just the file name, which might be artist - song.mp3, or artist - album - song.mp3, or another variation

A simple voting algorithm works fairly well, but I'd like to have something I can train on a large set of data that might pick up more nuances than what I've got right now. Any links to papers or similar projects would be greatly appreciated.

Thanks!

Hi all,

I'm looking for some guidance about which techniques/algorithms I should research for the following problem. I've currently got an algorithm that clusters extremely similar-sounding mp3s using acoustic fingerprinting. In each cluster, I have some metadata (song/artist/album) for each file. For that cluster, I'd like to pick the most representative song/artist/album that matches an existing row in my database, or if there is no best match, decide to insert a new row.

For a cluster, there is generally some correct metadata, but individual files have many types of problems: - Artist/songs are completely misnamed, or just slightly mispelled - the artist, song, or album is missing, but the rest of the information is there - the song is actually a live recording, but only some of the files in the cluster are labeled as such. In this case the song name should have (live) in it.
- there may be very little metadata, in some cases just the file name, which might be artist - song.mp3, or artist - album - song.mp3, or another variation

A simple voting algorithm works fairly well, but I'd like to have something I can train on a large set of data that might pick up more nuances than what I've got right now. Any links to papers or similar projects would be greatly appreciated.

Thanks!