Project overview
KEE Funded Project
This project aims to explore the expressive capacities of recent AI technologies in the context of music performance, specifically multimedia live installation and experimental musical theatre. Building on cutting-edge research in AI, I will compose a new piece for Ensemble Resilience in collaboration with artist Helen Knowles.
This original work will interrogate the role of AI technologies in interdisciplinary approaches to musical performance and composition. It will also cross-examine and encourage reflection and debate on the ethical consequences these new technologies have in our lives, placing a particular emphasis on the notion of bias and its real-life implications. The increasing processing power of modern computers (in particular GPUs), in conjunction with more and more efficient algorithms, has provided artists with a range of tools for fostering creativity and designing new forms of interaction. Generative AI and machine learning, in particular, have made a significant impact on the music industry and the way “techno-fluent” musicians make music today. In this project, I will use regression algorithms to command real-time audio processes (live electronics) and variational autoencoders for neural audio synthesis to generate vocal utterances that respond to musical gestures in real time. Facial recognition software will also be employed to enable one performer—the percussionist, Tomek Szczepaniak—to control sound and video materials in real-time through facial movements and gestures (e.g., opening and closing one or both eyes, opening and closing the mouth, moving the tongue, etc.). Tomek’s face will be projected on a transparent screen; this will be a central staging and theatrical element of the work. From an artistic research perspective, this project will investigate how these technologies mediate compositional and collaborative artistic practice, opening new possibilities for communication between disciplines and encouraging the search for innovative strategies (e.g., considering unconventional approaches to notate bodily gestures, movements and actions in configurations different from the conventional concert hall format). From a socio-cultural perspective, on the other hand, this project will tackle algorithmic bias and its role in reinforcing inequalities.
We will focus, in particular, on how underrepresentation in datasets disproportionally affects historically marginalised groups, reproducing injustices and strengthening discrimination (by sex, gender, age, class, income, ethnicity, race, etc.). We will use AI technologies to expose both their potential and flaws. By harnessing the capabilities of AI, we aim to create a unique artistic experience where human expression and algorithmic agency intertwine in an extended performative space. By combining elements of post-dramatic staging and experimental storytelling, we hope to raise awareness of the real-life consequences of algorithmic bias, inviting the audience to reflect on the implications of the unregulated use of these sophisticated and powerful tools.
This project aims to explore the expressive capacities of recent AI technologies in the context of music performance, specifically multimedia live installation and experimental musical theatre. Building on cutting-edge research in AI, I will compose a new piece for Ensemble Resilience in collaboration with artist Helen Knowles.
This original work will interrogate the role of AI technologies in interdisciplinary approaches to musical performance and composition. It will also cross-examine and encourage reflection and debate on the ethical consequences these new technologies have in our lives, placing a particular emphasis on the notion of bias and its real-life implications. The increasing processing power of modern computers (in particular GPUs), in conjunction with more and more efficient algorithms, has provided artists with a range of tools for fostering creativity and designing new forms of interaction. Generative AI and machine learning, in particular, have made a significant impact on the music industry and the way “techno-fluent” musicians make music today. In this project, I will use regression algorithms to command real-time audio processes (live electronics) and variational autoencoders for neural audio synthesis to generate vocal utterances that respond to musical gestures in real time. Facial recognition software will also be employed to enable one performer—the percussionist, Tomek Szczepaniak—to control sound and video materials in real-time through facial movements and gestures (e.g., opening and closing one or both eyes, opening and closing the mouth, moving the tongue, etc.). Tomek’s face will be projected on a transparent screen; this will be a central staging and theatrical element of the work. From an artistic research perspective, this project will investigate how these technologies mediate compositional and collaborative artistic practice, opening new possibilities for communication between disciplines and encouraging the search for innovative strategies (e.g., considering unconventional approaches to notate bodily gestures, movements and actions in configurations different from the conventional concert hall format). From a socio-cultural perspective, on the other hand, this project will tackle algorithmic bias and its role in reinforcing inequalities.
We will focus, in particular, on how underrepresentation in datasets disproportionally affects historically marginalised groups, reproducing injustices and strengthening discrimination (by sex, gender, age, class, income, ethnicity, race, etc.). We will use AI technologies to expose both their potential and flaws. By harnessing the capabilities of AI, we aim to create a unique artistic experience where human expression and algorithmic agency intertwine in an extended performative space. By combining elements of post-dramatic staging and experimental storytelling, we hope to raise awareness of the real-life consequences of algorithmic bias, inviting the audience to reflect on the implications of the unregulated use of these sophisticated and powerful tools.
Staff
Lead researchers
