‘Hit’ songs are big business, so there is an incentive for composers to try to tease out those ingredients that might increase their chances of success. However, it is no easy task due to the complexity of mixtures that go into making a song. But Natalia Komarova, a mathematician at the University of California, Irvine, thinks she has cracked the problem.
September 10th, 2018
Dr. Komarova and her colleagues collected information on music released in Britain between 1985 and 2015. They looked in public repositories of music “metadata” that are used by music lovers.
Metadata are information about the nature of a song that can give listeners an idea of what that song is like before they hear it. The repositories presented Dr. Komarova and her team with more than 500,000 songs that had been tagged by algorithms which had been trained to detect numerous musical features.
The team fed all of this information into a computer and compared the features of songs that had made it into the charts (roughly 4% of those in the repositories) with those of songs that had not.
Overall, the team’s results suggested that chart successes were happier and brighter (though also less relaxed), than the average songs released. Also, chart toppers were more likely than average songs to have been performed by women.
This article is a re-post, with small modifications, of “What Makes Good Music?” an article published on economist.com
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