From A Queen Song to A Better Music Search Engine (May 16, 2009) — UC San Diego electrical engineering Ph.D. student Luke Barrington presented a new model for music segmentation that can capture both the sound of a song and how this sound changes over time.

music-search-engineBarrington showed how to automatically segment songs such as Bohemian Rhapsody into homogenous sections such as verses, choruses and bridges.

This new approach to training computers to dissect songs into heterogeneous segments and then accurately label each chunk could improve the accuracy of the new music search engine built by engineers from the Jacobs School of Engineering at UC San Diego.

The team’s nickname for their experimental music search engine is “Google for music”. Users type descriptive words—rather than song titles, album names or artist names—and the search engine returns specific song suggestions.

The engine currently works for more than 100 words that cover music genres, emotions and instruments.

The Jacobs School engineers are working to expand the search engine’s “vocabulary” before opening it up to the public later this year.

In April, the electrical engineers launched their games on Facebook as an application called Herd It.

To play Herd It, log in to Facebook, open the Herd It app, select a genre of music, and start listening to song clips and playing the games. Some games ask users to identify instruments, while others focus on music genres, artist names, emotions triggered by the song, and activities you might do while listening to a song. The more your answers align with the rest of the online crowd playing the game at the same time, the more points you score.

“The Facebook games are a lot of fun and a great way to discover new music. At the same time, the games deliver the data we need to teach our computer audition system to listen to and describe music like humans do,” said Gert Lanckriet, the electrical engineering professor and machine learning expert from the Jacobs School of Engineering steering the project.

This exposure enables the machine learning algorithms find patterns in the wave forms of the songs that make the songs romantic. Once trained, the system can identify romantic songs that it has never before encountered, offering the tantalizing possibility of amassing a huge database of songs that can be tagged and retrieved based on text-based searches with no human intervention.

The song-word combinations collected by the Facebook games will also enable the researchers to grow their music search engine’s vocabulary and increase its coverage in genres and classes of music.

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