Deep neural networks for dynamic range compression in mastering applications

The process of audio mastering often, if not always, includes various audio signal processing techniques such as frequency equalisation and dynamic range compression. With respect to the genre and style of the audio content, the parameters of these techniques are controlled by a mastering engineer, in order to process the original audio material. This operation relies on musical and perceptually pleasing facets of the perceived acoustic characteristics, transmitted from the audio material under the mastering process. Modelling such dynamic operations, which involve adaptation regarding the audio content, becomes vital in automated applications since it significantly affects the overall performance. In this work we present a system capable of modelling such behaviour focusing on the automatic dynamic range compression. It predicts frequency coefficients which allow the dynamic range compression, via a trained deep neural network, and applies them to unmastered audio sign al served as input. Both dynamic range compression and the prediction of the corresponding frequency coefficients take place inside the time-frequency domain, using magnitude spectra acquired from a critical band filter bank, similar to human's peripheral auditory system. Results from conducted listening tests, incorporating professional music producers and audio mastering engineers, demonstrate on average an equivalent performance compared to professionally mastered audio content. Improvements were also observed, when compared to relevant and commercial software.