提出新滤波器实现音频RNN的采样率无损调节，解决模型固定采样率难题。
Interpolation Filter Design for Sample Rate Independent Audio Effect RNNs
- 用外推滤波器实现采样率降低，替代传统插值方法。
- 高阶滤波器可保持音质，但不当设计会引入失真信号。
- 通过稳定性分析预判滤波器适配性，指导实际部署选择。
循环神经网络（RNN）能有效模拟模拟吉他放大器和失真效果的非线性、状态依赖特性。与直接电路仿真不同，RNN的模型权重中编码了固定采样率，导致推理时无法调整。已有研究通过增加反馈延迟长度并使用分数延迟滤波器，在推理阶段实现采样率提升（过采样）。本文研究反向问题——推理阶段降低采样率（欠采样），提出采用外推滤波器近似所需分数信号提前。我们比较两种滤波器设计方法，并分析滤波器阶数对音质的影响。结果表明，恰当选择滤波器可在过采样与欠采样中均获得高质量输出；然而在某些情况下，采样率调整会导致输出信号出现非预期失真。通过线性化稳定性分析发现，这些失败源于围绕固定点的不稳定性。该方法可实现对特定RNN模型在运行前预测合适的插值滤波器。
原文摘要 · Abstract (English)
Recurrent neural networks (RNNs) are effective at emulating the non-linear, stateful behavior of analog guitar amplifiers and distortion effects. Unlike the case of direct circuit simulation, RNNs have a fixed sample rate encoded in their model weights, making the sample rate non-adjustable during inference. Recent work has proposed increasing the sample rate of RNNs at inference (oversampling) by increasing the feedback delay length in samples, using a fractional delay filter for non-integer conversions. Here, we investigate the task of lowering the sample rate at inference (undersampling), and propose using an extrapolation filter to approximate the required fractional signal advance. We consider two filter design methods and analyse the impact of filter order on audio quality. Our results show that the correct choice of filter can give high quality results for both oversampling and undersampling; however, in some cases the sample rate adjustment leads to unwanted artefacts in the output signal. We analyse these failure cases through linearised stability analysis, showing that they result from instability around a fixed point. This approach enables an informed prediction of suitable interpolation filters for a given RNN model before runtime.
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