The news: In a fresh spin on manufactured pop, OpenAI has released a neural network called Jukebox that can generate catchy songs in a variety of different styles, from teenybop and country to hip-hop and heavy metal. It even sings—sort of.
How it works: Give it a genre, an artist, and lyrics, and Jukebox will produce a passable pastiche in the style of well-known performers, such as Katy Perry, Elvis Presley or Nas. You can also give it the first few seconds of a song and it will autocomplete the rest.
Old songs, new tricks: Computer-generated music has been a thing for 50 years or more, and AIs already have impressive examples of orchestral classical and ambient electronic compositions in their back catalogue. Video games often use computer-generated music in the background, which loops and crescendos on the fly depending on what the player is doing at the time. But it is much easier for a machine to generate something that sounds a bit like Bach than the Beatles. That’s because the mathematical underpinning of much classical music lends itself to the symbolic representation of music that AI composers often use. Despite being simpler, pop songs are different.
OpenAI trained Jukebox on 1.2 million songs, using the raw audio data itself rather than an abstract representation of pitch, instrument, or timing. But this required a neural network that could track so-called dependencies—a repeating melody, say—across the three or four minutes of a typical pop song, which is hard for an AI to do. To give a sense of the task, Jukebox keeps track of millions of time stamps per song, compared with the thousand time stamps that OpenAI’s language generator GPT-2 uses when keeping track of a piece of writing.
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