Machine Learning in Electromechanical Sound Art

Abstract This article examines the use of neural networks in electromechanical sound art and music, where sound is materially enacted through physical means such as motors, solenoids, and physical resonators. It begins with a survey of documented works, outlining a range of current strategies and discussing how technical, material, and performative factors influence their design. Identifying natural language processing as underexplored in this domain, a practice-based case study, Seven Studies for Electric Motors , develops one such language-based approach. The project embeds a small language model for real-time sentence generation, extracts syntax structures, and translates these into patterns of motor-driven sound. Taken together, the survey and case study offer a picture of how machine learning has been integrated into electromechanical practices over the past decade and point to possible directions for further work.