Amuse To Use Machine Learning To Determine Royalty Advances
Getting an advance on your royalties is always a wild guess about what you’re worth. The label or publisher wants to keep it low, and the artist/songwriter wants it as high as possible in case it’s the last money he or she ever sees. The future of royalty advances may lie with artificial intelligence and machine learning however, and the Swedish distribution and artist services platform Amuse will soon go that way in determining the royalty advance payments for its independent acts.
Amuse’s Fast Forward service will offer indie acts up to six months of estimated future earnings, thanks to what the company learns about your streaming history from more than 27 billion online data points. Fast Forward members will then be able to view and withdraw future royalties up to 6 months in advance of actually earning them. The company uses machine learning to predict an artist’s future streaming performance in order to determine the available royalty advance.
Although Amuse acts as a standard digital distributor akin to CD Baby or Tunecore, it also offers a label-like licensing deal where the company provides a 50/50 split of the profits with the artist. The rights are returned to the artist after a set period of time.
While not as large as other online distributors, the company now has offices in Los Angeles and Bogotá along with teams in London and Miami.
This is just the beginning of the many instances where you’ll see machine learning used in the music business in the future. For the record, machine learning is a subset of artificial intelligence, which also includes other fields like image processing, cognitive science, neural networks and much more. AI deals with making machines, systems and other devices smart by giving them the ability to think and do tasks like humans do, whereas machine learning (ML) deals with making your machine learn from the external environment.
Sorry, but comments have been disabled due to the enormous amount of spam received. Please leave a comment on the social media post related to this topic instead.
