Aaron Einbond, City, University of London
Artemi-Maria Gioti, University College London
This project centres on reflexive auto-ethnographies of interdisciplinarity on the part of two composer-researchers (Aaron Einbond and Artemi-Maria Gioti) engaging with AI as a creative and collaborative tool. It asks: how can algorithmic compositional methods utilising AI be subverted creatively, and what is the role of interdisciplinary collaboration in this process? Questions of material engagement, musical labour, distributed creativity, and subjectification as they relate to AI are investigated in two interrelated studies exploring a range of interdisciplinary modes from ‘agonistic-antagonistic’ interactions between composer-researchers to ‘interdisciplinarity in one person’ (Barry and Born 2013).
The focus is the production of two compositions for acoustic instruments and electronics incorporating Machine Learning (ML): Aaron Einbond’s Prestidigitation, for percussion and 3-D electronics, and Artemi-Maria Gioti’s Bias II, for piano and interactive music system. Engaging with STS methodologies, the two studies go ‘inside’ the ML algorithms to explore how they ‘learn’ from Music Information Retrieval (MIR) data and human interpretative choices to challenge traditional notions of musical authorship and reshape the relationships between composers, performers, developers and listening subjects.
The project is based at UCL, with connections to and residencies at IRCAM (Institut de Recherche et Coordination Acoustique/Musique) in Paris (Einbond) and ZKM (Zentrum für Kunst und Medien) in Karlsruhe (Gioti), the latter via a ZKM commission following receipt of a Giga-Hertz production award. Both pieces result from collaborations with the performers: with percussionists Maxime Echardour and Benjamin Soistier (Einbond) and pianists Magda Mayas and Xenia Pestova Bennett (Gioti). The resulting musical performances and scores can be heard and viewed below:
Prestidigitation for Percussion and 3-D electronics, Benjamin Soistier (Percussion), Aaron Einbond (Composition and Electronics)
Other Related Disseminations
Déguernel, K., Sturm, Bob L., Gioti, A.-M., & Born, G. (2022) ‘Musical Interactivity in Human–AI and AI–AI Partnerships‘. Computer Music Journal 2022; 46 (4): 5–6. doi: https://doi.org/10.1162/comj_e_00656
Gioti, A.-M., Einbond, A., & Born, G. (2023). ‘Composing the Assemblage: Probing Aesthetic and Technical Dimensions of Artistic Creation with Machine Learning‘. Computer Music Journal pp. 1-43
Gioti A, Einbond A, Born G. (2022). Composing the Assemblage: Probing Aesthetic and Technical Dimensions of Artistic Creation with Machine Learning. Computer Music Journal, (4), doi: 10.1162/comj_a_00658
Performance/Broadcast:
Einbond, A. (2024). Prestidigitation II for percussion solo, bass flute, bass clarinet, violin, viola, violoncello, and three-dimensional electronics. Ensemble L’Instant Donné, produced by IRCAM/Centre Pompidou and Radio France, Festival Présences, Paris. Video and binaural recording broadcast by France Musique: https://youtu.be/eL6TooEwtHY
Einbond, A. (2022). Prestidigitation for percussion and 3D electronics. Benjamin Soitsier, Performing Critical AI, IKLECTIK Art Lab, London. Video and binaural recording: https://youtu.be/zi9COmpU9ms
Einbond, A. (2022). Prestidigitation for percussion and 3D electronics. Maxime Echardour, Mercredis de STMS, IRCAM, Paris, 2022. Video and binaural recording: https://youtu.be/j0kywabT0WQ
Published score:
Einbond, A. (2024). Prestidigitation II for percussion solo, bass flute, bass clarinet, violin, viola, violoncello, and three-dimensional electronics. Berlin and Brühl: Edition Gravis Verlag.
Einbond, A. (2022). Prestidigitation for percussion and three-dimensional electronics. Berlin and Brühl: Edition Gravis Verlag.
Conference paper/proceedings:
Einbond, A. (2024). Material Engagement with Machine Learning in Prestidigitation for percussion and 3-D electronics. The International Conference on AI and Musical Creativity. University of Oxford.