Ada's Song: Making machine-learning processes visible and tangible - Patricia ALESSANDRINI

Ada’s Song is a ca. 10-minute work for mezzo-soprano, ensemble and an interactive Piano Machine system, commissioned as an hommage to Ada Lovelace in 2019. It was created using AI-assisted composition processes, and employs real-time machine learning in the performance of the Piano Machine. Designed at Goldsmiths College in collaboration with Konstantin Leonenko in 2017, the Piano Machine plays the strings of the piano directly through mechanical, sustained vibration created by a set of motors and finger-like appendages controlled by microprocessors, thus creating dynamic control of notes over time, piloted by wireless OSC messaging. The material performed by the Piano Machine was generated by a concatenation of recordings of a work by Henry Purcell, Hosanna to the Highest, such that the repetitive ground bass of the original creates a foundation for the expressive intervention of real-time machine-learning processes.
In an attempt to render the Piano Machine more expressive and responsive to the ‘human’ musicians’ performance, the repeating harmonic patterns performed by the Piano Machine are shaped by machine learning processes that ‘listen’ to the instrumentalists during the rehearsals and performance. These processes filter the reservoir of notes and amplitudes produced from the concatenated recordings, not only in relation the notes that been played, but how they have been performed. This is achieved by building up training sets of timbral data over the course of rehearsals. Thus, the Piano Machine inscribes itself into the expressive sonic world of the ensemble.