Hard Data Predicts Why Songs Are Big Hits
August 26, 2020
Hollywood has a formula system to make blockbuster films and the music industry has something similar. It is harder to predict hit music than films, but Datanami believes someone finally has the answer: “Hooktheory Uses Data To Quantify What Makes Songs ‘Great’.”
Berkeley startup Hooktheory knows that many songs have similar melodies and lyrics. Hooktheory makes software and other learning materials for songwriters and musicians. With their technology, the startup wants to prove what makes music popular is quantifiable. Hooktheory started a crowdsourced database dubbed “Theorytabs” that analyses popular songs and the plan is to make it better with machine learning.
Theorytabs is a beloved project:
“The Hooktheory analysis database began as a “labor of love” by Hooktheory co-founders Dave Carlton, Chris Anderson and Ryan Miyakawa, based on the idea that “conventional tabs and sheet music are great for showing you how to play a song, but they’re not ideal for understanding how everything fits together.” Over time, the project snowballed into a community effort that compiled tens of thousands of Theorytabs, which Hooktheory describes as “similar to a guitar tab but powered by a simple yet powerful notation that stores the chord and melody information relative to the song’s key.”
Theorytabs users can view popular songs from idol singers to videogame themes. They can play around with key changes, tempos, mixers, and loops, along with listening to piano versions and syncing the songs up with YouTube music videos.
Hooktheory owns over 20,000 well-formatted tabs for popular music. The startup is working with Carnegie Mellon University and New York University to take Theorytabs to the next level. The music community has welcomed Theorytabs and people are eager to learn about the data behind great music.
Whitney Grace, August 27, 2020
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