Model and Deep learning based Dynamic Range Compression Inversion

Dynamic Range Compression (DRC) is a fundamental process in audio signal processing which aims at changing the dynamic range of a signal. This technique is widely used in various stages of audio production, such as recording, mixing, and mastering, to control the loudness of an audio signal and prevent clipping or distortion [1]. However, the application of DRC often leads to changes in the audio's timbre and perceived quality, making its inversion a challenging task. Thus, inverting DRC is full of interest in the context of audio reverse engineering [2] since it aims at recovering the original dynamic range and audio quality of a signal. This task could find many applications such as signal restoration, remixing, and enhancing creative control. Inverting DRC is a challenging problem which often requires side information with an explicit DRC model and prior knowledge about the DRC parameters to be efficiently processed. There only exist a few studies which directly address the problem of DRC inversion. In [3], the authors consider DRC inversion as a rate-distortion optimization problem using a coder-decoder framework which minimizes both the side-information and the reconstruction error when combined with a specific estimator applied to the compressed signal. In [4], the authors propose a specific DRC model which provides promising reconstruction approximation but require to know exactly the DRC parameters of the compressed signal.