无需梯度信息，用优化方法逆向恢复音频压缩参数。
Black-Box Optimization for Identifying and Inverting Audio Dynamic Range Control Effects
- 将压缩参数估计转为感知特征空间的黑箱优化问题。
- 在盲估计和干信号恢复上表现优于或媲美现有模型。
- 适合处理不可导的非线性音频效果，如基于直方图的描述符。
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，导致盲估计与逆向恢复极具挑战。本文将 DRC 参数估计建模为感知驱动特征空间中的黑箱优化问题。给定观测信号和参考表示，通过最小化重构信号与参考信号的特征描述符距离来估计参数。该方法无需 DRC 模型或特征提取流程的可微性，支持使用非线性及基于直方图的描述符。实验表明，所提方法在盲参数估计与干信号恢复任务中达到具有竞争力的性能，重建质量优于或媲美当前最优模型。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。