Brian Whitman landed at Spotify just in time. The MIT Media Lab alum and machine listening expert joined the product team at Spotify early last year when the streaming giant dropped a reported $100 million to acquire The Echo Nest, the music data company he cofounded a decade ago. Since his time at MIT, Whitman, along with cofounder and fellow PhD Tristan Jehan, has focused obsessively on the intersection of big data, artificial intelligence, and music, using that sweet spot to try and redefine music discovery in the age when songs flow freely like water and new artists pop up by the hour. Today, he’s sitting across from me in a conference room in Spotify’s New York headquarters showing me what his team has been building for the last few months.
It’s called Fresh Finds. At first glance, it’s just another playlist?it could easily be one of the collections hand-curated by Spotify’s 32 music editors, or one of its 75 million users for that matter. But Fresh Finds is different: The weekly playlist is generated using a chunk of the predictive big data technology that Whitman and his team at The Echo Nest brought with them to Spotify last March. The mission of Fresh Finds is to identify under-the-radar artists that are generating buzz online and surface the ones most likely to break out.
“Fresh Finds is a distillation of the hippest users on Spotify,” says Whitman, pulling up a list of 38 tracks projected against the conference room wall. “These are the artists that are going to break out soon because they’re being listened to by these people.”
I don’t recognize any of the artists on the list. Neither did he, Whitman admits. But now many of them have made their way into his daily rotation. “Just wait a few weeks and people will start talking more and more about them and they’ll take off.”
How does he know? The machines told him, naturally. Fresh Finds takes a central component of The Echo Nest’s original methodology?its web content crawler and natural language processing technology?to mine music blogs and reviews from sites like Pitchfork and NME and figure out which artists are starting to generate buzz, but don’t yet have the listenership to show for it. Using natural language processing, the system analyzes the text of these editorial sources to try and understand the sentiment around new artists. For instance, a blogger might write that a band’s “new EP blends an early ’90s throwback grunge sound with mid-’80s-style synthesizers and production?and it’s the best thing to come out of Detroit in years.” If this imaginary act goes on tour and writers in Brooklyn dole out praise of their own, the bots will pick up on it. It helps address an issue some people have voiced early on with Apple Music, that its selections aren’t adventurous and it tends to recommend things you already like rather than things you might like.
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