"“Coditany of Timeness” is a convincing lo-fi black metal album, complete with atmospheric interludes, tremolo guitar, frantic blast beats and screeching vocals. But the record, which you can listen to on Bandcamp, wasn’t created by musicians."
"Instead, it was generated by two musical technologists using a deep learning software that ingests a musical album, processes it, and spits out an imitation of its style. To create Coditany, the software broke “Diotima,” a 2011 album by a New York black metal band called Krallice, into small segments of audio. Then they fed each segment through a neural network — a type of artificial intelligence modeled loosely on a biological brain — and asked it to guess what the waveform of the next individual sample of audio would be. If the guess was right, the network would strengthen the paths of the neural network that led to the correct answer, similar to the way electrical connections between neurons in our brain strengthen as we learn new skills. At first the network just produced washes of textured noise. “Early in its training, the kinds of sounds it produces are very noisy and grotesque and textural,” said CJ Carr, one of the creators of the algorithm. But as it moved through guesses — as many as five million over the course of three days — the network started to sound a lot like Krallice. “As it improves its training, you start hearing elements of the original music it was trained on come through more and more.” Coditany of Timeness is part of a side project by Carr, a startup CTO, and Zack Zukowski, a music producer. The pair met as undergrads at Northeastern University during a program at Berklee College, a prominent music school in Boston, and quickly bonded over a shared interest in programmatic composition and machine learning. Coditany of Timeness was the first of three programmatic albums Dadabots has released using this technique, and arguably most successful."
"We use a modified SampleRNN [1] architecture to generate music in modern genres such as black metal and math rock. Unlike MIDI and symbolic models, SampleRNN generates raw audio in the time domain. This requirement becomes increasingly important in modern music styles where timbre and space are used compositionally. Long developmental compositions with rapid transitions between sections are possible by increasing the depth of the network beyond the number used for speech datasets. " (paper)
small segments of audio
Three albums have been produced so far.