# Resampling Filter Design for Multirate Neural Audio Effect Processing

arXiv: https://arxiv.org/abs/2501.18470 · HTML: https://mlsanity.com/paper/2501.18470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [Deep Learning for Tube Amplifier Emulation](https://mlsanity.com/paper/1811.00334/) (2018)
- [Efficient neural networks for real-time modeling of analog dynamic range compression](https://mlsanity.com/paper/2102.06200/) (2021)
- [Perceptual Loss Function for Neural Modelling of Audio Systems](https://mlsanity.com/paper/1911.08922/) (2019)
- [Modelling black-box audio effects with time-varying feature modulation](https://mlsanity.com/paper/2211.00497/) (2022)
- [Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing](https://mlsanity.com/paper/2406.06293/) (2024)
- [End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models](https://mlsanity.com/paper/2403.08559/) (2024)
- [Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models](https://mlsanity.com/paper/2411.14972/) (2024)
- [Distortion Recovery: A Two-Stage Method for Guitar Effect Removal](https://mlsanity.com/paper/2407.16639/) (2024)
- [Interpolation Filter Design for Sample Rate Independent Audio Effect RNNs](https://mlsanity.com/paper/2409.15884/) (2024)

## Built on by (most cited first, 1 of 1)

- [Anti-aliasing of neural distortion effects via model fine tuning](https://mlsanity.com/paper/2505.11375/) (2025)

## Abstract (arXiv)

Neural networks have become ubiquitous in audio effects modelling, especially for guitar amplifiers and distortion pedals. One limitation of such models is that the sample rate of the training data is implicitly encoded in the model weights and therefore not readily adjustable at inference. Recent work explored modifications to recurrent neural network architecture to approximate a sample rate independent system, enabling audio processing at a rate that differs from the original training rate. This method works well for integer oversampling and can reduce aliasing caused by nonlinear activation functions. For small fractional changes in sample rate, fractional delay filters can be used to approximate sample rate independence, but in some cases this method fails entirely. Here, we explore the use of real-time signal resampling at the input and output of the neural network as an alternative solution. We investigate several resampling filter designs and show that a two-stage design consisting of a half-band IIR filter cascaded with a Kaiser window FIR filter can give similar or better results to the previously proposed model adjustment method with many fewer filtering operations per sample and less than one millisecond of latency at typical audio rates. Furthermore, we investigate interpolation and decimation filters for the task of integer oversampling and show that cascaded half-band IIR and FIR designs can be used in conjunction with the model adjustment method to reduce aliasing in a range of distortion effect models.