AI 中文总结
研究音频动态范围压缩参数未知的问题，将其参数估计转化为黑盒优化问题，通过在感知特征空间中估计参数来最小化重建与参考信号特征描述符距离，该方法性能有竞争力，优于或匹配现有模型。
AI 中文摘要
动态范围压缩（DRC）是一种广泛使用的非线性音频效果，其参数通常未知，这使得盲估计和反转具有挑战性。在这项工作中，我们将DRC参数估计表述为在感知驱动的特征空间中的黑盒优化问题。给定一个观测信号和一个参考表示，我们估计使重建信号和参考信号的特征描述符之间的距离最小化的参数。与基于梯度的方法不同，该方法不需要DRC模型或特征提取管道的可微性，能够使用非线性和基于直方图的描述符。实验结果表明，该方法在盲参数估计和干信号恢复方面具有有竞争力的性能，在重建质量方面优于或匹配现有模型。
英文摘要
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.
