Dadabots
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About
Dadabots is a project focused on using neural networks to generate music, primarily in the black metal genre. They create raw audio neural networks, write code, publish science, do collaborations, and play shows. Their work involves training AI models on raw audio waveforms of metal albums, allowing the AI to hallucinate and create its own music. The project aims to explore the potential of AI in music creation and collaborate with artists to push the boundaries of musical expression.
Platform
Task
Features
• music generation
• raw audio neural networks
• collaboration with bands
• livecoding
• infinite remix
• ai song contest
• neural networks generating black metal
FAQs
What is DADABOTS?
Not sure what DADABOTS is. We're a cross between a band, a hackathon team, and an ephemeral research lab. We're musicians seduced by math. We do the science, we engineer the software, we make the music.
How did you get started working on DADABOTS?
First day we met in 2012 CJ said "Zack I feel like I’ve known you my whole life". We formed a hackathon team called Dadabots. This was Music Hack Day MIT. We were intrigued with the pointlessness of machines generating crappy art.
What inspired you to program an AI to create/replicate music?
We saw the potential of deep neural networks when image style transfer was released. It was amazing when we saw photographs transform into impressionist oil paintings.
How does creating your music with the NSynth algorithm work?
We don't use NSynth. NSynth generates very short samples of monophonic instruments. We used SampleRNN for our bandcamp albums.
How does creating music with SampleRNN work?
We started with the original SampleRNN research code in theano. It's a hierarchical LSTM network. LSTMs can be trained to generate sequences. Sequences of whatever. Could be text. Could be weather.
Why have you done this?
Mischief.
Do you plan on using this software for retail purposes?
Neural synthesis will inevitably be a part of next generation audio software. There's no question. Right now the hardware is prohibitively expensive (we use V100 GPUs).
Do you think Music generated by neural networks will have the potential to reach mainstream success?
Becoming mainstream has been important for subcultures that were underrepresented and needed a voice. Teenagers. African-Americans. Etc. Whereas tech culture already dominates the world.
Is there any specific reason why you are focusing on math rock and black metal to generate, instead of other, more mainstream genres?
For some reason, other a.i. music people are trying to do mainstream. Mainstream music is dead. Solid. Not alive. Rigor Mortis. Any new music idea it gets has been harvested from the underground.
When did it feel like something you could pull off as independent musicians, or that the music you were making was worth sharing publicly on Bandcamp, etc.?
We were in bands, we liked extreme music, we wanted to make music forever, but we were faced with the existential problem of our inevitable deaths.
What were those initial teaching sessions like? How did you guys break down duties as producers and programmers?
Bizarre.
What’s the difference about your approach to generating music and other methods of computer generated music like randomizing MIDI inputs?
We train it completely unsupervised. There's no knowledge of music theory. There's no MIDI. There's nothing. It's just raw audio. It's surprising that it works in the first place.
What drew you to working with audio rather than these other pretty popular kinds of MIDI approaches?
Midi is only 2% of what there is to love about music.. You can’t have Merzbow as midi. Nor the atmosphere of a black metal record. You can’t have the timbre of Jimi Hendrix’s guitar, nor Coltrane’s sax, nor MC Ride.
Why is it harder?
Raw audio has 44100 time steps a second. These are huuuge sequences. Up to 10,000x larger than sheet music. We needed heavy duty hardware only recently available to us plebes.
The tagline on the Bandcamp page is 'We write programs to develop artificial artists'; what's the plan for the algorithm in the long run?
But our aim is human augmentation.
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