Losses, Dissonances, and Distortions
This work addresses the niche problem of interactive art creation for performers and audiences, but it is incremental as it applies existing ML methods to a new artistic domain without broader technical advancements.
The paper tackled the problem of integrating machine learning training dynamics into artistic performance by using losses and gradients from a function approximator to generate musical dissonance and visual distortion in a solo piano setting, resulting in a closed feedback loop where the performer influences the training process.
In this paper I present a study in using the losses and gradients obtained during the training of a simple function approximator as a mechanism for creating musical dissonance and visual distortion in a solo piano performance setting. These dissonances and distortions become part of an artistic performance not just by affecting the visualizations, but also by affecting the artistic musical performance. The system is designed such that the performer can in turn affect the training process itself, thereby creating a closed feedback loop between two processes: the training of a machine learning model and the performance of an improvised piano piece.