Getting Computers Into the Groove

Automated song analysis could lead to better recommendations for listeners.

Computers have revolutionized the production, distribution and consumption of music, but when it comes to recommending a good tune, they're still sorely lacking.

Sounds alike: A Facebook game called Herd It is being used by researchers at the University of California, San Diego, to classify music into different genres. Credit: Facebook/UCSD

There are plenty of recommendation systems out there. iTunes offers Genius, which creates playlists and suggests music by comparing a collection to those of other users, and numerous music-oriented social-networking sites offer recommendations inspired by what a person's friends are listening to. Now researchers at the University of California, San Diego (UCSD), are using machine learning, in combination with a Facebook game, to classify music based on automated analysis of the songs.

Gert Lanckriet, an assistant professor at UCSD, who is working on the project, says that the automated approach taken by his group's music search and recommendation engine means that it could analyze huge quantities of songs, potentially giving users recommendations from a much larger library of music. The system can also make judgments about songs that it's never come across before.

The UCSD researchers want their system to be able to tag songs so that users can search not just by artist or song title, but also by genre, instrument, and even descriptive words such as "romantic" or "spooky." With this goal in mind, they're collecting information about songs using a Facebook application called Herd It. The game awards users points when they tag songs in ways that agree with other users' tags, collecting massive amounts of data in the process.

Once that data is collected, Lanckriet says, the researchers' system groups songs according to the tags given to them by users, then searches for distinctive patterns in the music itself. It applies a statistical analysis to the waveform patterns that represent each song, looking for common features among songs grouped together by tag.

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About 90 percent of the time, Lanckriet says, the system identifies patterns that are ordinarily hidden. For example, the patterns that identify a hip-hop song might include a typical hip-hop beat, but also elements that the listener wouldn't recognize as a pattern within the song. "On average, these automatic tags predict other humans' [tags] pretty much as accurately as a given human person can do," Lanckriet says.

The researchers are currently working on collecting more data to train their system, and Lanckriet believes that the system has commercial potential. He envisions a system that could take an unfamiliar song--from an independent band, or even something recorded in a user's garage--and then analyze it on the fly and suggest appropriate tags and similar music.