PhDThis research explores the possibility of reproducing mixing decisions of a skilled audio
engineer with minimal human interaction that can improve the overall listening experience of
musical mixtures, i.e., intelligent mixing. By producing a balanced mix automatically
musician and mixing engineering can focus on their creativity while the productivity of music
production is increased. We focus on the two essential aspects of such a system, frequency
and dynamics. This thesis presents an intelligent strategy for multitrack frequency and
dynamics processing that exploit the interdependence of input audio features, incorporates
best practices in audio engineering, and driven by perceptual models and subjective criteria.
The intelligent frequency processing research begins with a spectral characteristic analysis of
commercial recordings, where we discover a consistent leaning towards a target equalization
spectrum. A novel approach for automatically equalizing audio signals towards the observed
target spectrum is then described and evaluated. We proceed to dynamics processing, and
introduce an intelligent multitrack dynamic range compression algorithm, in which various
audio features are proposed and validated to better describe the transient nature and spectral
content of the signals. An experiment to investigate the human preference on dynamic
processing is described to inform our choices of parameter automations. To provide a
perceptual basis for the intelligent system, we evaluate existing perceptual models, and
propose several masking metrics to quantify the masking behaviour within the multitrack
mixture. Ultimately, we integrate previous research on auditory masking, frequency and
dynamics processing, into one intelligent system of mix optimization that replicates the
iterative process of human mixing. Within the system, we explore the relationship between
equalization and dynamics processing, and propose a general frequency and dynamics
processing framework. Various implementations of the intelligent system are explored and
evaluated objectively and subjectively through listening experiments.China Scholarship Council
