“They know what songs you like, even before you like them. Record labels spend millions of dollars each year trying to predict what singles will top the charts and which ingredients make a hit single. Now, two Massachusetts Institute of Technology PhD grads believe they have cracked the code,” Grant Robertson reports for The Globe and Mail.
“After years of crunching data, Brian Whitman and Tristan Jehan have devised a computer program that listens to a song, then predicts how humans will react to it,” Robertson reports. “The response is so specific at times that it can forecast how a single will perform on the charts and spit out a review, guessing what words will be used to describe it, from ‘sexy to romantic to loud and upbeat,’ Mr. Whitman said.”
“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,” Robertson reports. “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.”
Robertson reports, “If it sounds far-fetched, consider this: the system has been predicting Billboard hits with surprising accuracy over the past several months. While people may think their musical tastes are unpredictable and whimsical, they are actually quite traceable, Mr. Whitman says. The researchers’ goal is to revolutionize the tracking techniques used by companies such as Amazon.com and Apple Computer Inc.’s iTunes music store. Those companies compare similarities between songs, add in the buying history of consumers, then recommend albums that each person should buy. Mr. Whitman and Mr. Jehan, who are both musicians, scoff at those methods.”
Full article here.
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