Can we use AI + neuroscience to predict which songs will be hits? Maybe.
[This was my weekly column for GlobalNews.ca. – AC]
There’s a scene in the biopic Creation Stories where Ewan Brenner, channelling Creation Records founder Alan McGee in a scene with his therapist, rants about the demands of finding The Next Big Thing in music. “I’m spending millions on noises that I have no idea if anyone will like!” Welcome to the record business.
What makes a song a hit? No one knows. It’s a mysterious organic process that no one has been able to unlock.
One record company president described things like this: “Running a record label is based on risk. We rely on creative types — musicians — to eventually supply us with songs that we hope the public will like. A song can be objectively great but if the public doesn’t bite, there’s no amount of money we can throw at marketing and promotion to make them like it.”
This hasn’t stopped people from trying to come up with a way to accurately predict hits.
When rock’n’roll was still young, a couple of promoters got it in their heads that the process of writing hit songs could be distilled down to a formulaic process. In 1959, Joe Mulhall and Paul Neff sent out a questionnaire to 3,000 girls about their likes and dislikes when it came to music. Their thinking was that if they could incorporate as many positive data points as they could into a song, then they’d be guaranteed to have a hit for their wannabe pop star, a 15-year-old American weightlifter named Johnny Restivo. When all the responses were collated, this song was the result.
