A couple of grad students at MIT have developed a computer program that can analyze a song and then predict whether or not it will be a hit. The system is so accurate that it’s been able to forecast the recordings that hit the Billboard charts over the past few months.

But other scientists have created devices for analyzing music and predicting how humans will react to the rhythms and other factors. What makes this process different is that the MIT researchers are using web socialization tools to weave in some very non-scientific data.

The MIT method, developed at the school’s renowned Media Laboratory, also takes into account social responses to hit music that are fed into the algorithms.

The researchers pull data from weblogs, chat rooms and music reviews — anywhere a song is being discussed — and feed it into the computer, which allows the software to gauge the popularity of a certain sound.

Once all the information is tabulated, the computer can listen to an entirely new album and predict how people will respond based on what it knows about the latest reactions to the music it has already heard.

While I find the science and the use of RSS in this project fascinating, it’s also rather depressing. A good chunk of popular music is already homogenized and bland. Putting this in the hands of big media will only make it harder to find something that’s not pre-programed and formulaic.

music, web 2.0, mit