打造可微分的音频效果建模框架，支持黑箱与灰箱模型研究。
NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects
- 基于PyTorch构建，支持可微分黑箱与灰箱音频效果建模。
- 集成多种架构、数据集与训练流程，便于对比实验。
- 适合音频信号处理与可微分音频建模方向的研究者使用。
我们提出NablAFx，一个开源框架，用于支持音频效果的可微分黑箱与灰箱建模研究。该框架基于PyTorch，提供灵活的生态系统，可用于配置、训练、评估和比较不同架构。包含模型架构、数据集与训练管理类，支持损失、指标与媒体的计算与记录，并提供绘图功能以促进深入分析。框架集成已有黑箱架构与条件化方法，以及可微分的数字信号处理模块与控制器，支持构建参数化与非参数化的灰箱信号链。代码已公开于https://github.com/mcomunita/nablafx。
原文摘要 · Abstract (English)
We present NablAFx, an open-source framework developed to support research in differentiable black-box and gray-box modeling of audio effects. Built in PyTorch, NablAFx offers a versatile ecosystem to configure, train, evaluate, and compare various architectural approaches. It includes classes to manage model architectures, datasets, and training, along with features to compute and log losses, metrics and media, and plotting functions to facilitate detailed analysis. It incorporates implementations of established black-box architectures and conditioning methods, as well as differentiable DSP blocks and controllers, enabling the creation of both parametric and non-parametric gray-box signal chains. The code is accessible at https://github.com/mcomunita/nablafx.
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