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.
(See document for exact affiliation information.)
https://aes2.org/publications/elibrary-page/?id=18770
(631KB)
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.
