Machine listening aesthetics: reanimating sonic ecologies

A broad range of critique in cultural studies of technology is concerned with the social implications of AI and its social biases. Nonetheless, this strand of critical thought often lacks a thorough understanding and technical analysis of the inner workings of current techniques in the computational sciences. This is particularly the case in the domain of listening, where computation is used to automate sound pattern recognition and event detection in what is known as the perceptual analogue to machine vision: machine listening. I respond to this gap by investigating machine listening via two core concepts: soundscape addressability and algorithmic filtering.