Abstract
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.
Abstract (translated)
音乐混音的逆向工程旨在揭示干声源信号是如何被处理和组合以生成最终混音的。我们扩展了先前的工作，反映混合的组成特性，并寻找音频处理器图。首先，我们构建一个混音控制台，在每个轨道和子组上应用所有可用的处理器。利用可微分的处理器实现方式，我们用梯度下降法优化它们的参数。然后，我们会重复删除不重要的处理器并微调剩余处理器的过程。这样可以在移除约三分之二的处理器的同时保持整个混音控制台的质量。 提出的方法不仅可以用于分析单个音乐混音，还可以收集大规模图数据以供下游任务使用，例如自动混音。特别是为了后者的用途，高效的搜索实现至关重要。为此，我们提供了一种高效的批量处理方法，在其中可以并行计算多个处理器。此外，我们利用处理器的“干/湿”参数来加速搜索过程。 进行了广泛的定量和定性分析以评估所提出的方法性能、行为及其计算成本。
URL
https://arxiv.org/abs/2509.15948
