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.
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.
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.
It’s a long way from the days of talent scouts combing smoky bars for the next big sound.
But computer analysis of songs is not necessarily new. A wide variety of companies spend hours in laboratories breaking down hit songs so the music industry can stay one step ahead of the market.
The goal is to pinpoint trends in pitch, rhythm and cadence that are driving consumer spending habits. However, the MIT researchers believe they’ve taken the science to another level.
“Some people really care about instrument sounds and complexity of the music,” Mr. Whitman said. “But the 14-year-old teenage girl could care less, as long as her friends are listening to it.”
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.
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.
“They say you bought this so you’ll like this. But it’s really bad for music because it can only recommend stuff that people have bought a lot of,” he said.
Still, the music industry has been trying for decades to come up with a reliable system. The standard practice today is to crunch data from focus groups across a broad spectrum of tastes, which gives hints of a song’s true potential in the market.
New York-based HitPredictor has built its business crunching weekly data from focus groups, and many of the play lists heard on North American radio are influenced by the company.
HitPredictor polls thousands of listeners each week on songs that have not yet been released, then makes prognostications on how the single will perform.
The company established its credibility in 2002 when RCA used its method to determine the order in which the singles from Christina Aguilera’s album Stripped should be released to maximize record sales. Since then, other labels have turned into regular customers.
After crunching feedback data on the Aguilera album, HitPredictor realized RCA needed to rethink the release order because the focus groups were unexpectedly reacting favourably to some songs, but not others. Each prediction the company made in terms of how well each single would sell eventually proved true in the market.
“A lot of labels put music through our research to confirm their instincts,” said Doug Ford, co-founder of HitPredictor. “They’ve got a few guys in management that like this song, but marketing likes that song, so they go through us.”