Tuesday, September 25, 2007

"Google for Music"

When engineers put the words "indie" and "Backstreet Boys" in the same sentence in an academic paper, you got to take notice.

"For example, a pre-teen girl might consider a Backstreet Boys song to be ‘touching and powerful’ whereas a dj at an indie radio station may consider it ‘abrasive and pathetic’."

-from Identifying Words that are Musically Meaningful, by David Torres, Doug Turnbull, Luke Barrington and Gert Lanckriet from UC San Diego’s Jacobs School of Engineering.

The UC San Diego electrical engineers and computer scientists used the Backstreet Boys to highlight the subjective nature of music. This subjectivity makes it tricky to teach a computer to label songs with words. Should the computer label a Backstreet Boys song as if it were a pre-teen girl? or a dj at an indie radio station? If you want to find out, check out the paper. It is one of three papers the UCSD group is presenting at a music information retrieval conference (ACM ISMIR) this week in Vienna.

Beyond the Backstreet Boys, the researchers are working on a music search engine that lets you type in words and get acutal songs in return. One of the main things they are reporting at the conference in Vienna is that their online game (it's called Listen Game) is good for collecting data that they need to train their computer system to automatically label songs (with no input from humans, except for the initial training.) Read the full press release here.

They are now working on new versions of the games. The original version, Listen Game, works well, but, as the authors told me, "it was made my a bunch of engineers." (Read: it looks like an old-school video game and it doesn't exactly measure up to today's coolest online games.)

You can check out the old version of Listen Game

If you want to get an email (no spam, I promise) when the new version of the game comes out, send an email to

Papers presented at ISMIR 2007:
A Game-Based Approach for Collecting Semantic Annotations of Music


Identifying Words that are Musically Meaningful

A Supervised Approach for Detecting Boundaries in Music using Difference Features and Boosting

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