通过可微分处理器与迭代剪枝，逆向解析音乐混音的处理图谱。
Reverse Engineering of Music Mixing Graphs with Differentiable Processors and Iterative Pruning
- 构建包含所有处理器的混音调音台，用梯度下降优化参数。
- 移除约三分之二冗余处理器后，仍保持混音质量不变。
- 适合研究混音机制或开发自动混音系统的研究者使用。
音乐混音的逆向工程旨在揭示干声信号如何被处理并组合成最终混音。本文扩展了先前工作，以反映混音的组合特性，搜索音频处理器的图结构。首先，构建一个混音调音台，将所有可用处理器应用于每条音轨和子组；通过可微分处理器实现，使用梯度下降优化其参数。随后，重复移除可忽略的处理器并微调剩余处理器的过程。该方法在保留完整混音调音台质量的同时，移除了约三分之二的处理器。所提方法不仅可用于分析单个音乐混音，还可用于收集大规模图数据，支持下游任务如自动混音。为此，我们提出一种高效批量处理方法，可并行计算多个处理器，并利用处理器的“干/湿”参数加速搜索。通过大量定量与定性分析，评估了方法的性能、行为及计算成本。
原文摘要 · Abstract (English)
Reverse engineering of music mixes aims to uncover how dry source signals are processed and combined to produce a final mix. We extend the prior works to reflect the compositional nature of mixing and search for a graph of audio processors. First, we construct a mixing console, applying all available processors to every track and subgroup. With differentiable processor implementations, we optimize their parameters with gradient descent. Then, we repeat the process of removing negligible processors and fine-tuning the remaining ones. This way, the quality of the full mixing console can be preserved while removing approximately two-thirds of the processors. The proposed method can be used not only to analyze individual music mixes but also to collect large-scale graph data that can be used for downstream tasks, e.g., automatic mixing. Especially for the latter purpose, efficient implementation of the search is crucial. To this end, we present an efficient batch-processing method that computes multiple processors in parallel. We also exploit the "dry/wet" parameter of the processors to accelerate the search. Extensive quantitative and qualitative analyses are conducted to evaluate the proposed method's performance, behavior, and computational cost.
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