Automated Multi-Track Mixing and Analysis of Instrument Mixtures

Abstract

Access to hardware and software tools for producing music has become commonplace in the digital landscape. While the means to produce music have become widely available, significant time must be invested to attain professional results. Mixing multi-channel audio requires techniques and training far beyond the knowledge of the average music software user. Achieving balance and clarity in a mixture comprising a multitude of instrument layers requires experience in evaluating and modifying the individual elements and their sum. Creating a mix involves many technical concerns (level balancing, dynamic range control, stereo panning, spectral balance) as well as artistic decisions (modulation effects, distortion effects, side-chaining, etc.). This work proposes methods to model the relationships between a set of multi-channel audio tracks based on short-time spectraltemporal characteristics and long term dynamics. The goal is to create a parameterized space based on high level perceptual cues to drive processing decisions in a multi-track audio setting.