将可微音频模型编译为实时音频插件，自动保持精度与稳定性。
Compiling Differentiable Audio Graphs to Real-Time DSP
- 通过中间表示将训练好的模型转为高效FAUST代码
- 输出结果与原模型冲击响应误差小于浮点数噪声水平
- 支持实时调试和稳定性的自动验证，适合音频研发者
可微音频处理器通常在机器学习框架中设计和优化，但部署为实时音频效果时仍需手动编写专用数字信号处理语言代码。该过程易出错、验证成本高，且使研究原型难以转化为可用工具。为此，我们提出ADAC编译器，将训练好的模型降级为框架无关的中间表示，并生成高效的FAUST代码，其冲击响应与源模型的差异仅在浮点数计算噪声范围内，包含直达路径。通过在每次梯度更新后替换运行中的插件，优化过程可被听觉感知。导出的处理器包含少量宏控制参数，确保稳定性。构建前会基于已发送参数进行稳定性验证。演示中，反馈延迟网络被训练并成功导出为可用插件。
原文摘要 · Abstract (English)
Differentiable audio processors are habitually designed and optimised in machine-learning frameworks, but deploying them as real-time audio effects still often requires non-automatic implementation in a dedicated digital signal processing language. The translation is error-prone, demands an onerous verification process, and detaches research prototypes from usable production tools. That being so, we present ADAC, a compiler that lowers a trained model to a framework-agnostic intermediate representation and emits efficient FAUST code whose impulse response matches the source model to within floating-point arithmetic noise, direct paths included. The optimisation loop is made audible by replacing the model in a running plugin after each gradient step. The exported processor carries a small set of macro-controls that leave its stability intact. A stability certificate computed from the shipped parameters is checked before the plugin is built. At the demonstration, a feedback delay network is trained and exported to a working plugin.
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。