In this study, we present an online music production tool that facilitates the capture of time-series audio and session data, including action history. This allows us to analyse sessions and infer production decisions based on actions made to the user interface. We conduct an experiment in which mix engineers were asked to use the system to perform a balance mix, then we provide observations made using the system. We show that participants often exhibit commonalities in mixing styles when applying gain and panning to specific instruments in a mix, and demonstrate common temporal characteristics relating to the magnitude of parameter adjustments.

Authors: Jillings, Nicholas; Stables, Ryan
Affiliation: Birmingham City University, Birmingham, UK
AES Conference: 2017 AES International Conference on Semantic Audio (June 2017)
Paper Number: P1-7
Publication Date: June 13, 2017 Import into BibTeX
Subject: Semantic Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=18770

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