Neural Synthesis as a Methodology for Art-Anthropology in Contemporary Music

Abstract

This article investigates the use of machine learning within contemporary experimental music as a methodology for anthropology, as a transformational engagement that might shape knowing and feeling. In Midlands (2019), Sam Salem presents an (auto)ethnographical account of his relationship to the city of Derby, UK. By deriving musical materials from audio generated by the deep neural network WaveNet, Salem creates an uncanny, not-quite-right representation of his childhood hometown. Similarly, in her album A Late Anthology of Early Music Vol. 1: Ancient to Renaissance (2020), Jennifer Walshe uses the neural network SampleRNN to create a simulated narrative of Western art music. By mapping her own voice onto selected canonical works, Walshe presents both an autoethnographic and anthropological reimagining of a musical past and questions practices of historiography. These works are contextualised within the practice and theory of filmmaker-ethnographer Trinh T. Minh-ha and her notion of ‘speaking nearby’. In extension of Tim Ingold’s conception of anthropology, it is shown that both works make collaborative human and non-human inquiries into the possibilities of human (and non-human) life.