# Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing

arXiv: https://arxiv.org/abs/2406.06293 · HTML: https://mlsanity.com/paper/2406.06293/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2024.

## Built on by (most cited first, 7 of 7)

- [Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models](https://mlsanity.com/paper/2411.14972/) (2024)
- [Resampling Filter Design for Multirate Neural Audio Effect Processing](https://mlsanity.com/paper/2501.18470/) (2025)
- [Designing Neural Synthesizers for Low-Latency Interaction](https://mlsanity.com/paper/2503.11562/) (2025)
- [Anti-aliasing of neural distortion effects via model fine tuning](https://mlsanity.com/paper/2505.11375/) (2025)
- [ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications](https://mlsanity.com/paper/2506.12665/) (2025)
- [Parametric Neural Amp Modeling with Active Learning](https://mlsanity.com/paper/2507.02109/) (2025)
- [Parametric Neural Amp Modeling with Active Learning](https://mlsanity.com/paper/2509.26564/) (2025)

## Abstract (arXiv)

In recent years, machine learning approaches to modelling guitar amplifiers and effects pedals have been widely investigated and have become standard practice in some consumer products. In particular, recurrent neural networks (RNNs) are a popular choice for modelling non-linear devices such as vacuum tube amplifiers and distortion circuitry. One limitation of such models is that they are trained on audio at a specific sample rate and therefore give unreliable results when operating at another rate. Here, we investigate several methods of modifying RNN structures to make them approximately sample rate independent, with a focus on oversampling. In the case of integer oversampling, we demonstrate that a previously proposed delay-based approach provides high fidelity sample rate conversion whilst additionally reducing aliasing. For non-integer sample rate adjustment, we propose two novel methods and show that one of these, based on cubic Lagrange interpolation of a delay-line, provides a significant improvement over existing methods. To our knowledge, this work provides the first in-depth study into this problem.