Adaptive Filtering for Multi-Track Audio Based on Time–Frequency-Based Masking Detection

There is a growing need to facilitate the production of recorded music as independent musicians are now key in preserving the broader cultural roles of music. A critical component of the production of music is multitrack mixing, a time-consuming task aimed at, among other things, reducing spectral masking and enhancing clarity. Traditionally, this is achieved by skilled mixing engineers relying on their judgment. In this work, we present an adaptive filtering method based on a novel masking detection scheme capable of identifying masking contributions, including temporal interchangeability between the masker and maskee. This information is then systematically used to design and apply filters. We implement our methods on multitrack music to improve the quality of the raw mix.