Art Music Is – It was only five years ago that the electronic punk band YACHT entered the recording studio with a daunting task: they taught an artificial intelligence their music for 14 years and then synthesized the results into the album “Chain Tripping”.
“I don’t like to be reactionary,” YACHT member and tech writer Claire L. Evans said in a documentary about the album. “I don’t want to go back to my roots and play acoustic guitar because I’m afraid of the coming robot apocalypse, but I also don’t want to jump into a ditch and greet our new robot overlord.”
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But our new robot overlords are making great strides in the next generation of AI music. Although the Grammy-nominated “Chain Tripping” was released in 2019, the technology behind it has been around for a long time. Now, the startup behind the open source AI image generator Stable Diffusion is pushing us forward again with the next step: making music.
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Harmonai is an organization funded by Stability AI, a London-based startup called Stable Diffusion. At the end of September, Harmonai released Dance Diffusion, a set of algorithms and tools that can create music clips by training hundreds of hours of existing songs.
“I started working with sound diffusion at the same time I started working on Stability AI,” said Zach Evans, head of development at Dance Diffusion, in an email interview. “I was brought to the company through development work with [image generation algorithm] Disco Diffusion and quickly decided to focus on good research. To facilitate my learning and research, and to create a community focused on AI audio, I founded Harmonai.
Diffusion Dance is in testing phase – currently the system can only generate clips for a few seconds. But the initial results provide a tantalizing look at what the future of music creation may hold, while raising questions about the potential impact on artists.
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Dance Diffusion comes a few years after OpenAI, the San Francisco-based lab behind DALL-E 2, detailed its big experiment in music generation called Jukebox. Given the genre, artist and piece of text, Jukebox can create relatively cohesive music with vocals. However, songs created by Jukebox lack a larger musical structure, such as repetitive choruses and often contain gibberish.
Google’s AudioLM, detailed for the first time earlier this week, shows promise with its amazing ability to produce piano music given short pieces of playback. But it is not open source.
Diffusion Dance aims to overcome the limitations of previous open source tools by borrowing technology from image generators such as Stable Diffusion. These systems are called diffusion models that generate new data (such as songs) by learning to destroy and rebuild many existing data samples. Given an existing sample—for example, the entire Smashing Pumpkins discography—the model gets better at recovering previously destroyed data to create new works.
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Kyle Worrall, Ph.D. A student at the University of York in the UK, who is researching musical applications of machine learning, explained the nuances of the diffusion system in an interview:
“Diffusion model training involves ‘destroying’ and ‘rebuilding’ training data, such as the MAESTRO data from piano performances, and the model improves on these tasks as it passes through the training data,” he said. by e-mail. “Finally, the trained model can take the sound and turn it into music similar to the training data (i.e. a piano performance in MAESTRO’s case). The user can then use the trained model to perform one of three tasks: create a new sound, create an existing sound user-selected, or interpolate between two input tracks.
It’s not the most intuitive idea. But as DALL-E 2, Stable Diffusion and other similar systems show, the results can be very realistic.
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“Our initial reaction was, ‘Okay, this is a leap forward from where we were before with raw audio,'” Bechtolt said.
Unlike other popular image generation systems, Dance Diffusion is, at least for now, quite limited in what it can create. While it can be customized for a specific artist, genre, or instrument, the system isn’t as generic as Jukebox. A handful of available Dance Diffusion models – early adopters of Harmonai and official Discord servers, including refined models with clips from Billy Joel, The Beatles, Daft Punk and musician Jonathan Mann’s Song A Day project – remain in their respective models. route This means that the Jonathan Mann model will always produce songs in Mann’s musical style.
And the music created by Dance Diffusion isn’t fooling anyone these days. While the system can style a song by applying the style of one artist to another, basically creating a cover, it cannot create a clip of a few seconds or lyrics that are not gibberish (see the clip below). This is the result of a technical hurdle that Harmonai has yet to overcome, says Nicolas Martel, a self-taught game developer and member of the Harmonai Discord.
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“This model is only trained for short examples of 1.5 seconds at a time, so it cannot learn or reason about long-term structures,” said Martel. “The author seems to say that this is not a problem, but in my experience – and logically, anyway – that is not true.”
YACHT’s Evans and Bechtolt worry about the ethical implications of AI — they are, after all, working artists — but note that this “style transition” is already part of the natural creative process.
“It’s something artists have done in the studio in a more informal and sloppy way,” says Evans. “You sit down to write a song and you want, I want the bass line to fall and the B-52 melody, and I want it to sound like it came from London in 1977.”
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But Evans was not interested in writing a dark, post-punk rendition of “Love Shack”. Instead, he believes that interesting music comes from experimentation in the studio—even if you draw inspiration from the B-52s, your final product will show no signs of that influence.
“Trying to achieve that, you fail,” Evans said. “One of the things that attracted us to machine learning tools and the art of artificial intelligence is the way they fail because these models are not perfect. They only think what we want.”
Evans describes artists as “the ultimate beta testers,” using tools outside of the way they were designed to create new things.
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“Often the output can be really weird and damaged and disturbing, or it can sound really weird and novel, and that failure is fun,” Evans says.
Assuming that Diffusion Dance will one day go as far as making coherent wholes, it seems inevitable that larger ethical and legal issues will arise. They already have, albeit a simpler AI system. In 2020, Jay-Z’s record label filed a copyright infringement lawsuit against YouTube channel Vocal Synthesis for using artificial intelligence to cover Jay-Z songs such as Billy Joel’s “We Didn’t Start the Fire”. After initially removing the video, YouTube reinstated it, finding that the takedown request was “insufficient.” But deep-fake music still stands on murky legal ground.
Perhaps ahead of the legal challenge, OpenAI for its part has open-sourced Jukebox under a non-commercial license, prohibiting users from selling music created by the system.
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“There is little work to establish the originality of the output of generative algorithms, so the use of generative music in advertising and other projects remains at risk of inadvertently infringing copyright and damaging property,” said Worrall. “This area needs further research.”
An academic paper, authored by Eric Sunray, currently a legal intern at the Music Publishers Association, states that AI music generators such as Dance Diffusion infringe music copyright by creating “coherent sound tapestry of works taken from practice, in violation of United States copyright. law.” copyright. Since the release of Jukebox, critics have also questioned whether the training of AI models on copyrighted music is fair use. Similar problems have been raised about training data used by AI systems that produce images, code and text, often scraped. from the web without the knowledge of the creator.
Techies like Mat Dryhurst and Holly Herndon founded Spawning AI, a set of AI tools created by artists. One of his projects, “Have I Been Trained,” allows users to search for their artwork and see if it has been added to an AI training suite without their permission.
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“We’re showing people what’s in the popular datasets used to train AI imaging systems and initially giving them the tools to opt out or participate in training,” Herndon said via email. “We also talked to many of the largest scientific organizations to convince them that the consensus data is good for everyone.”
But these standards are, and likely will remain, voluntary. Harmonai has not said if she will adopt them.
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