Every day, tens of thousands of songs are released.
This constant stream of options makes it difficult for streaming services and radio stations to choose which songs to add to playlists.
Researchers at Claremont University have now applied machine learning (ML) to high-frequency neurophysiologic data to improve hit song prediction.
During the experiment, the scientists measured the energy levels and brain-reactions of 33 individuals responding to 24 songs.
They found that a linear statistical model identified hit songs at a success rate of 69%. When they applied machine learning to the data they collected, the rate of correctly identified hit songs jumped to 97%.MORE