Cultural practice in general is a slippery but deeply felt affair, where practitioners are often tasked with communicating the »underside of meaning« (Barthes, 1976). In algorithmic times, focusing on this task when working with machine learning outputs can offer an alternative to the pressure to consider quantifiable and easily machine-readable datasets as complete repositories of human knowledge. The multimodal knowledge held in sound and human experience is difficult to parse with data tools, e.g. machine learning, but the failures and messiness of these outputs add to our toolsets of constructing and reconstructing the worlds we offer in our stories. Through a presentation of some of Sinha’s discoveries navigating the worlds of theatre and machine learning-based audio research-creation, this audio paper offers examples of the possibilities of engaging with algorithmic failure as a deliberate strategy of discovery, to uplift the exploration of cultural possibilities and engage alternative models of community building and connection.

Audio paper by Debashis Sinha.

Part of Seismograf Peer: Sound and the More-Than-Human Worlds

Algorithmic mythologies: Failure as a cultural tool

Seismograf is an independent Danish web magazine focusing on the newest developments within the arts of sound.