Automatic Minimisation of Masking in Multitrack Audio using Subgroups
This work addresses the challenge of optimizing audio mixing for improved clarity and quality, though it appears incremental as it builds on existing psychoacoustic models and automatic mixing techniques.
The authors tackled the problem of minimizing auditory masking in multitrack audio mixing by proposing a new masking metric and evaluating automatic mixing with subgroups, resulting in reduced inter-channel masking and improved perceived quality and clarity of mixes.
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.