Wednesday 6 December 2023 from 18.00 to 19.30
Lecture held in English
Belval, Maison des Sciences Humaines, Black Box
Prof. Luc Nijs
Associate professor in Early Childhood Music Education
Faculty of Humanities, Education and Social Sciences
Department of Education and Social Work
The emergence of the Digital Era has revolutionised the world of music. Digital technologies and their adventurous adoption by artists have led to new musical languages and forms of expression, to new ways of playing and sharing music. In addition, digital technologies promote access to music, and one might assume digital practices can transform the way we learn and teach music.
What are the implications and potentialities of the changes for music education?
Among scholars and music educators who embrace digitisation, some envision technology as a way to enhance traditional music education, others as an avenue to reimagine it altogether. Using examples from research and practice, this talk will provoke critical reflection on the nature of music education and its current evolution.
Federico Visi will present a performance focused on extending the embodied relationship between musician and instrument through machine learning and feedback loops. The interplay between human and non-human agencies will be a catalyst for an improvisation aimed at exploring different gestural configurations between body and instrument.
Federico Visi (he/they) is a researcher, composer and performer based in Berlin, Germany. He carried out his doctoral research on instrumental music and body movement at the Interdisciplinary Centre for Computer Music Research (ICCMR), University of Plymouth, UK. He currently teaches and carries out research on embodiment and interaction in network music performance at Universität der Künste Berlin and at Luleå University of Technology, where is part of the “GEMM. Gesture Embodiment and Machines in Music” research cluster. Under the moniker AQAXA, they released an EP in which they combine conventional electronic music production techniques with the exploration of personal sonic memories by means of body movement and machine learning algorithms.