Researchers at Claremont University (United States) developed an algorithm trained with neurological data from users to predict whether a song will be successful or not.
The experts from this institution have come to predict with a 97 percent accuracy rate whether a topic will be liked by the vast majority of users, using the listeners’ neurophysiological responses according to different analysis models.
For the sample, 33 participants between the ages of 18 and 57 were chosen, who listened to a total of 24 recent songs -13 of them considered hits and with more than 700,000 listeners in ‘streaming’ and 11 of them failures- and who were asked about their tastes and impressions of each of them.
Researchers at Claremont University set out to demonstrate that neurophysiological measures remarkably accurately identify hit songs, while users’ self-reported liking is not predictive.
Combined neurophysiology with machine learning creates an algorithm that “substantially” improves the classification of hit songs compared to traditional linear statistical models.
The study authors concluded that neurophysiological responses to the first minute of songs predicted hits with a success rate of 82 percent, indicating that the first part of a song largely determines its popularity.
In their conclusions, the researchers comment that their intent with this study is “to show that neuroscience measures of the peripheral nervous system fairly accurately classify hits and misses” and that this approach can assess content value automatically.
“If our findings are replicated, the ability to curate music and other forms of entertainment to give people what they want will enhance existing recommendation engines that will benefit artists, distributors, and consumers,” the researchers concluded.
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How to know if a song will be successful: listen up musicians!