In sound production, engineers cascade processing modules at various points in a mix to apply audio effects to channels and busses. Previous studies have investigated the automation of parameter settings based on external semantic cues. In this study, we provide an analysis of the ways in which participants apply full processing chains to musical audio. We identify trends in audio effect usage as a function of instrument type and descriptive terms, and show that processing chain usage acts as an effective way of organising timbral adjectives in low-dimensional space. Finally, we present a model for full processing chain recommendation using a Markov Chain and show that the system’s outputs are highly correlated with a dataset of user-generated processing chains.

Download