Bias II (2022) // for piano and interactive music system
Bias II for piano and interactive music system is part of a series of works engaging with the materiality and limitations of Machine Learning (ML) algorithms and data. During its interactions with different pianists, the computer music system collects data pertaining to the way performers navigate a set of 7 timbral clusters (pools of timbrally similar musical actions). Each second of the performance, a Recurrent Neural Network (RNN) trained on these data predicts how the performance might continue (i.e., which timbre is likely to follow next) and plays back sound material based on its predictions. Historical data, collected by the computer music system in past performances, influence the system's future behavior, setting the performer in an explicit dialogue with the work's interpretative history.
Credits:
The ML algorithm used in this piece was trained on data collected with pianists Magda Mayas and Xenia Pestova-Bennett.
This work was funded by ZKM Karlsruhe and the ERC advanced grant "MusAI - Music and Artificial Intelligence: Building Critical Interdisciplinary Studies" (innovation programme under European Research Council grant agreement no. 101019164).
Performance by Magda Mayas at ZKM Karlsruhe: