Dynamic Range Compression (DRC) is a popular audio effect used to control the dynamic range of a signal. Inverting DRC can also help to restore the original dynamics to produce new mixes and/or to improve the overall quality of the audio signal. Since, state-of-the-art DRC inversion techniques either ignore parameters or require precise parameters that are difficult to estimate, we fill the gap by combining a model-based approach with neural networks for DRC inversion. To this end, depending on the scenario, we use different neural networks to estimate DRC parameters. Then, a model-based inversion is completed to restore the original audio signal. Our experimental results show the effectiveness and robustness of the proposed method in comparison to several state-of-the-art methods, when applied on two music datasets.

翻译：动态范围压缩（DRC）是一种广泛使用的音频效果，用于控制信号的动态范围。对DRC进行逆变换有助于恢复原始动态特性，以生成新的混音版本和/或提升音频信号的整体质量。由于现有的先进DRC逆变换技术要么忽略参数，要么需要难以精确估计的准确参数，本文通过将基于模型的方法与神经网络相结合进行DRC逆变换，填补了这一空白。为此，我们根据不同场景使用不同的神经网络来估计DRC参数，随后通过基于模型的逆变换完成原始音频信号的还原。在两个音乐数据集上的实验结果表明，与多种先进方法相比，所提方法具有显著的有效性与鲁棒性。