According to new research from scientists in California, a robot utilizing artificial intelligence (AI) could be the next step in identifying hit pop songs and artists in the music industry. The scientists said that by utilizing the technology, they’ve been able to identify hit songs with 97% accuracy.
“By applying machine learning to neurophysiologic data, we could almost perfectly identify hit songs. That the neural activity of 33 people can predict if millions of others listened to new songs is quite amazing. Nothing close to this accuracy has ever been shown before,” says Paul Zak, a professor at Claremont Graduate University and senior author, in a media release.
The AI itself uses a neural network, which is apparently so straightforward that it can also be utilized for streaming service efficiency, TV shows, and movies in general.
The music industry today is dominated by streaming services. With billions of songs to choose from, it can become challenging for popular apps such as Spotify, Apple Music, Tidal, etc. to choose which ones their users will listen to, especially among newer artists.
Professor Zak claims that his colleagues and himself believe that their method is twice as effective as previous models which only showed a 50% success rate.
In the study itself, participants listened to a set of 24 songs while wearing a skull-cap brain scanner. Throughout the process, they were asked about their preferences while the scientists measured their neurophysiological responses.
“The brain signals we’ve collected reflect activity of a brain network associated with mood and energy levels,” Zak stated.
Based on the responses, the team of scientists were able to use their technology to predict market outcomes for certain songs, including the number of streams a song may receive. This process is referred to as “neuroforecasting,” which essentially means using the brain activity of a select group of people to predict how a larger population will react.
According to reports from Study Finds, who reported on the study, “a statistical model identified potential chart hits 69 percent of the time, but this jumped to 97 percent when machine learning was applied to the data. The team found that even by analyzing neural responses to only the first minute of songs, they achieved a success rate of 82 percent.”
“This means that streaming services can readily identify new songs that are likely to be hits for people’s playlists more efficiently, making the streaming services’ jobs easier and delighting listeners,” Zak explains.
“If in the future wearable neuroscience technologies, like the ones we used for this study, become commonplace, the right entertainment could be sent to audiences based on their neurophysiology. Instead of being offered hundreds of choices, they might be given just two or three, making it easier and faster for them to choose music that they will enjoy.
“Our key contribution is the methodology. It is likely that this approach can be used to predict hits for many other kinds of entertainment too, including movies and TV shows,” Zak stated.
Eric Mastrota is a Contributing Editor at The National Digest based in New York. A graduate of SUNY New Paltz, he reports on world news, culture, and lifestyle. You can reach him at firstname.lastname@example.org.