What question did this study set out to answer?

The aim is to investigate how machine learning, particularly neural networks, can be applied in electromechanical sound art.

April 8, 2026Open Access

Machine Learning in Electromechanical Sound Art

Key Points

The aim is to investigate how machine learning, particularly neural networks, can be applied in electromechanical sound art.
Conducted a survey of existing works in electromechanical sound art.
Developed a practice-based case study titled 'Seven Studies for Electric Motors.'
Embedded a small language model for real-time sentence generation and syntax extraction.
Identified underutilization of natural language processing in sound art.
Demonstrated integration of language-based approaches in motor-driven sound creation.
Outlined potential directions for future research in this intersection.

Abstract

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.

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