Applications of swarm aesthetics in music composition are not new and have resulted in volumes of complex soundscapes and musical compositions. However, sound composition using physical swarm agents has not been extensively studied. Using an experimental approach, Mahsoo Salimi and Philippe Pasquier create a series of sound textures know as Liminal Tones (A/Autumn Swarm) based on swarming behaviours. These will be detailed further in the paper titled Exploiting Swarm Aesthetics in Sound Art, presented at Art Machines 2: International Symposium on Machine Learning and Art 2021, scheduled to take place between 10th-14th June 2021.
Sound as a conceptual medium is influencing our art culture. Many contemporary artists began to explore sound in its pure state, simultaneously bridging and blurring the notion of sound, noise, and music. In the past few decades, there have been several approaches using robotics, mechatronics and artificial intelligence (AI) for developing musical improvisations, sonification, orchestra, and sound art. The goal in most cases is to push the boundaries of conventional music and explore the infinite possibilities of randomness, chance, noise sounds, and glitch. Furthermore, robotic and electromechanical machines with embedded automation and performative capabilities have the capacity to extend the musical creation process.
Mahsoo’s research into the materiality of swarm aesthetics, Liminal Tones (B/ Rain Dream) will also be presented at the International Conference on Swarm Intelligence, taking place July 17-21, 2021 in Qingdao, China and virtually online. The paper details further the experimental methods used in the Liminal Tones project.
2021 | Salimi, M., Pasquier, P. “Liminal Tones: Swarm Aesthetics and Materiality in Sound Art“. in Proceedings of the International Conference on Swarm Intelligence (ICSI’21) (2021).
2021 | Salimi, M., Pasquier, P. “Exploiting Swarm Aesthetics in Sound Art“. in Proceedings of the Art Machines 2: International Symposium on Machine Learning and Art 2021, Art Machines 2 (2021).