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This study addresses the challenge of estimating and inverting Dynamic Range Compression (DRC) effects, which are often applied without known parameters. By framing the problem as a black-box optimization task within a perceptually motivated feature space, the authors successfully estimate DRC parameters by minimizing the distance between feature descriptors of the observed and reference signals. The method shows competitive performance against state-of-the-art techniques, particularly excelling in blind parameter estimation and signal recovery without relying on gradient-based approaches.
Blindly estimating DRC parameters can now be achieved without requiring differentiability, leading to superior signal recovery outcomes.
Dynamic Range Compression (DRC) is a widely used nonlinear audio effect whose parameters are often unknown, making blind estimation and inversion challenging. In this work, we formulate DRC parameter estimation as a black-box optimization problem in a perceptually motivated feature space. Given an observed signal and a reference representation, we estimate the parameters that minimize the distance between feature descriptors of the reconstructed and reference signals. Unlike gradient-based approaches, the proposed method does not require differentiability of the DRC model or the feature extraction pipeline, enabling the use of nonlinear and histogram-based descriptors. Experimental results demonstrate that the proposed method achieves competitive performance in blind parameter estimation and dry signal recovery, outperforming or matching state-of-the-art models in terms of reconstruction quality.