New Ai Platform Protects Musicians’ Work – Just five years ago, the electronic punk band YACHT entered the recording studio with a difficult task: they would train an AI in music for 14 years, then synthesize the results into the album “Chain Tripping”.

“I’m not interested in being reactionary,” says YACHT member and tech writer Claire L. Evans in a documentary about the album. “I don’t want to go back to my roots and play acoustic guitar because I’m too scared of the coming robot apocalypse, but I also don’t want to jump into the trenches and welcome our new robot overlord.”

New Ai Platform Protects Musicians’ Work

But our new robot ruler is making big strides in the AI ​​music generation space. Although the Grammy-nominated “Chain Tripping” was released in 2019, the technology is getting old. Now, the startup behind the open source AI image generator Stable Diffusion brings us back with the next step: making music.

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Harmonai is a company with financial backing from Stability AI, the London-based startup behind Stability Diffusion. At the end of September, Harmoni released Dance Diffusion, an algorithm and tool that can generate music clips by practicing hundreds of hours of songs.

“I started work on audio diffusion at the same time as Stability AI,” said Jack Evans, Dance Diffusion’s head of development, in an email interview. “I was brought to the company because of my development work with [image generation algorithm] Disco Diffusion, and I quickly decided to pivot to audio research. To facilitate my own learning and research, and to create a community focused on audio AI, I started Harmonai.

Dance Diffusion is still in the testing phase – currently, the system can only generate clips that are a few seconds long. But the early results give a good picture of the future of music creation, while also raising questions about the potential impact on artists.

The emergence of Dance Diffusion comes a few years after OpenAI, the San Francisco-based laboratory behind DALL-E 2, described a brilliant experiment with a music product called Jukebox. Given a genre, artist, and lyric snippet, a jukebox can produce music that is relatively coherent in sound. But the jukebox-produced songs do not have a large musical structure, such as the chorus, which has repetitive and often nonsensical lyrics.

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Google’s AudioLM, first detailed earlier this week, shows more promise with its incredible ability to reproduce pieces of piano music played. But not open source.

Dance Diffusion aims to overcome the limitations of previous open source tools by borrowing techniques from image generators such as Stable Diffusion. The system is called a diffusion model, which describes how to destroy and recover many samples of existing data to generate new data (for example, songs). Given an existing sample—say, the entire Smashing Pumpkins discography—the model gets better at capturing all the corrupted data before creating a new work.

Kyle Worrall, Ph.D. student at the University of York in England, studying the musical application of machine learning, explained the nuances of the diffusion system in an interview:

“In training a diffusion model, training data such as the MAESTRO dataset of piano performances is ‘destroyed’ and ‘restored,’ and the model gets better at the task as it runs through the training data,” he said. email The trained model can create noise and manipulate the same music as the training data (ie, a piano performance in MAESTRO’s case). The user can use the trained model to perform one of three tasks: generate new audio, recreate user-selected audio, or interpolate between two input tracks.

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It’s not the most obvious idea. But as shown by DALL-E 2, Stable Diffusion, and other systems, the results are very real.

“Our initial reaction was, ‘Well, this is a step forward from where we were before with raw audio,'” Bechtolt said.

Unlike popular image generation systems, Dance Diffusion is somewhat limited in what it can create – at least for now. Although it is tuned to a specific artist, genre or instrument, the system is not as general as a jukebox. Several Dance Diffusion models are available from Harmonai and early adopters on the official Discord server, including well-tuned models with clips from Billy Joel, The Beatles, Daft Punk, and musician Jonathan Mann’s Song A Day project. path. In other words, Jonathan Mann’s model always produces songs in Mann’s musical style.

And the music produced by Dance Diffusion isn’t fooling anyone these days. The system can “style transfer” a song by applying the style of another artist to one song, especially creating a cover, which can produce a clip of no more than a few seconds and an indecent lyric (see the clip below). This is the result of a technical hurdle that Harmonai has yet to overcome, said Nicolas Martel, a self-taught game developer and member of the Harmonai Discord.

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“The model is only trained on small samples 1.5 seconds at a time, so it doesn’t learn or account for long-term structure,” Martell said. “The author seems to say that this is not a problem, but in my experience – and logically – this is not true.”

YACHT’s Evans and Bechtolt worry about the ethical implications of AI — they’re working with artists — but they note that this “transfer of style” is already part of the natural creative process.

“It’s something artists do in the studio informally and casually,” says Evans. “You sit down to write a song and you’re like, I want a falling bass line and a B-52 melody, and I want it to sound like it’s from London in 1977.”

But Evans wasn’t interested in writing a dark, post-punk rendition of “Love Shack.” However, he thinks that interesting music comes from experimentation in the studio – even if you’re inspired by the B-52s, your final product may not show any signs of that influence.

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“When you try to reach out, you fail,” Evans said. “One of the things that drew us to machine learning tools and the art of AI is the way they can fail, because the models are not perfect. They guess what they want.”

Evans describes artists as “the ultimate beta testers” who use tools outside their intended means to create something new.

“Often, the output can be weird and broken and disturbing, or it can feel weird and novel, and that failure is fun,” Evans said.

Assuming that the spread of dance will reach a point where all coherent songs are formed, it is inevitable that major ethical and legal issues will arise. They already have a simple AI system. In 2020, Jay-Z’s record label filed a copyright attack against YouTube channel Vocal Synthesis for using AI to create covers of Jay-Z songs such as Billy Joel’s “We Didn’t Start the Fire”. After removing the video, YouTube took it back, calling the request “incomplete”. But deepfaked music is still on murky legal ground.

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Perhaps anticipating legal challenges, OpenAI for its part has open-sourced Jukebox under a non-commercial license, prohibiting users from selling any music created with the system.

“There is little work done to ensure how original the output of the production calculation, so the use of production music in advertising and other projects open the risk of accidental copyright infringement and property damage,” said Worrall. “This area deserves more research.”

An academic paper by Eric Sunray, now a legal intern at the Music Publishers Association, says that AI music generators like Dance Diffusion violate music copyright, “by creating coherent audio tapestries from trained tasks. Legal reproduction rights. After Jukebox was released, critics also questions whether training AI models on copyrighted music material is used fairly. Similar concerns have been raised about training data used by AI systems that generate images, code and text, which are often removed from the web without the knowledge of the creators.

Technologists like Matt Dryhurst and Holly Herndon founded Spawning AI, a set of AI tools built by artists for artists. One project, “I’m Trained”, allows users to search for artwork and see if it’s included in an AI training set without consent.

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“We show people what’s in the popular datasets used to train AI image systems and initially give them tools to opt out or not participate in training,” Herndon said via email. “We also spoke with many of the largest research institutions to convince them that the consensus data is useful for everyone.”

But the standards are — and will be — voluntary. Harmoni has not said whether it will be adopted.


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