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Machine learning can now help craft the perfect breakup playlist

Machine learning can now help craft the perfect breakup playlist
[Photo: Eric Nopanen/Unsplash]

Nielsen-owned Gracenote is launching a new service called Sonic Style, an AI-fueled music descriptor system that will help the music industry make the perfect soundtracks for breakups, retirement parties, cross-fit workouts, or anything else.

Sonic Style is hoping to shake up the way music is traditionally categorized, moving past the typical overarching genres like rock or hip-hop to classify each song on a granular level. To that end, Gracenote has amassed nearly 450 Sonic Style descriptors that create a “style profile” of each recording and can pair machine learning descriptors such as tempo and mood with editorial ones, like artist genre, era, and origin. For example, the press release suggests the service can mine through Taylor Swift’s catalogue to figure out which songs are more pop, more country, more pop electronica, or god help us, more R&B. That level of musical understanding will hopefully help smart speakers, voice assistants, streaming services, and, yes, even measly humans create better, more personalized playlists.

The secret to the program? Machine learning. “Sonic Style applies neural network-powered machine learning to the world’s music catalogs, enabling Gracenote to deliver granular views of musical styles across complete music catalogs,” says Brian Hamilton, Gracenote’s general manager of music and auto, in a statement. “These new turbo-charged style descriptors will revolutionize how the world’s music is organized and curated, ultimately delivering the freshest, most personalized playlists to keep fans listening.”

By applying machine learning to music, Gracenote Sonic Style can quickly classify entire music catalogs, and can link to musical artists and songs across TV, VOD catalogs, and streaming services.

Nielsen acquired Gracenote from Tribune Media in 2016.

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