  The iterative process of masking minimisation when mixing multitrack audio is
a challenging optimisation problem, in part due to the complexity and
non-linearity of auditory perception. In this article, we first propose a
multitrack masking metric inspired by the MPEG psychoacoustic model. We
investigate different audio processing techniques to manipulate the frequency
and dynamic characteristics of the signal in order to reduce masking based on
the proposed metric. We also investigate whether or not automatically mixing
using subgrouping is beneficial or not to perceived quality and clarity of a
mix. Evaluation results suggest that our proposed masking metric when used in
an automatic mixing framework can be used to reduce inter-channel auditory
masking as well as improve the perceived quality and perceived clarity of a
mix. Furthermore, our results suggest that using subgrouping in an automatic
mixing framework can be used to improve the perceived quality and perceived
clarity of a mix.
