Music and Digital Humanities

Autoethnography of a Data-making Practice with Artemi-Maria Gioti (Mozarteum University Salzburg)

The Distinguished Lecture Series Music and Digital Humanities at mdw — University of Music and Performing Arts Vienna invites leading international experts in diverse aspects of DH to share their perspectives with our students, faculty, and community. The series is aimed at a broad, non-technical audience. It provides a varied overview of the history and current state of DH as it applies to music, its philosophical underpinnings and societal implications, and is expected to yield insights into relevant methodologies, technologies, infrastructures, and applications working with humanities datasets.

Topics include data management and computational analysis for digital musicology, digital editions, DH and artificial intelligence, machine learning and music information retrieval, as well as pedagogy, science communication, and citizen science. The series is convened by Chanda VanderHart and David M. Weigl, digital musicology researchers at the mdw's Department for Music Acoustics — Wiener Klangstil, and organized in collaboration with the mdw's Department of Musicology and Performance Studies.

Lectures will be presented in English.

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This project is funded by CLARIAH-AT with support from the BMFWF.

Programm

In this talk, Artemi-Maria Gioti will draw on autoethnographic notes from the compositional process, rehearsals, and performances of her work Bias II for piano and interactive music system to examine the critical insights that artistic research in composition can offer into machine learning and data practices. She will focus specifically on the critical perspectives that emerged through the creation of datasets, the training of machine learning models, and their deployment in live performance settings. Bringing an autoethnography of the data-making practices involved in the work into dialogue with theoretical frameworks from critical data studies, she will propose a deconstructive critique of data as material, processual, and relational, and foreground the aesthetic decisions embedded in them. Finally, a speculative error analysis of one of the machine learning models deployed in the piece will serve as a site of critical inquiry into the epistemological assumptions underlying machine learning systems.

Artemi-Maria Gioti is a composer and artistic researcher conducting critical research at the intersection of music and artificial intelligence (AI). She is Professor of Artistic Research in Music at Mozarteum University Salzburg. Her compositional work focuses on interactive works involving reciprocal, real-time interaction between human performers and computer music systems incorporating machine learning (ML). 

Further infos can be found here.