Lure is an artistic research project related to machine learning in theatre and music. We have explored a variety of machine learning techniques in combination with musical instruments, voice and narration. The artistic research transfers questions on the topics of human–machine interaction and artificial intelligence to the fields of theatre and music.
"Machine Learning for Musicians and Artists" is a course hosted on Kadenze.com and taught by Dr. Rebecca Fiebrink. It proved to be instructive and inspiring and helped effectively for the communication with specialists from the field. It's a good intro if you’re new to the area and interested to see how simpler ML algorithms can be related to music and performance art.
Reflections on conducting research on joint artistic and technical development processes of interdisciplinary collaborations.
Already in 1990, musical experiments with analog neural networks have been made. David Tudor, a major figure in the New York experimental music scene, collaborated with Intel to build the very first analog neural synthesizer.
MLure.art is a central blog covering artistic research projects related to Machine Learning (= Artificial Intelligence) in theatre and music. Most of the projects are initiated by artist and composer Mathis Nitschke. People involved are Elsa Büsing, Klasien van de Zandschulp, Luciano Pinna, Patrick van der Smagt, Djalel Benbouzid and Boton Cseke. The research is funded and supported by the Dutch Stimuleringsfonds, the German Fonds Darstellende Künste and the Volkswagen Group Basic Machine Learning Research Lab ARGMAX. Drawing by Katharina Dobner
An idea for an interactive installation made of laser and light
How can we make notation relevant to today’s music practice? Some composers are using technology to enable new notation methods.
Ever wondered in which of the devices and gadgets you're surrounded with Artificial Intelligence is already incorporated? Here's a collection.
The Free Energy Principle - Neuroscientist Karl Friston on the Markov blanket, Bayesian model evidence, and different global brain theories. How is the brain a statistical model of the world it inhabits?
One of our research approach starts with the notion that language evolved as a subset of music. So first was music, then language. A notion that already Heiner Müller expressed when he said that it's not the singing that starts when words end, but exactly the opposite.
What would the music sound like if its creator wouldn't know anything about our venerable higher concepts of music?