# Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling

arXiv: https://arxiv.org/abs/2408.04829 · HTML: https://mlsanity.com/paper/2408.04829/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [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)

Recurrent neural networks (RNNs) have demonstrated impressive results for virtual analog modeling of audio effects. These networks process time-domain audio signals using a series of matrix multiplication and nonlinear activation functions to emulate the behavior of the target device accurately. To additionally model the effect of the knobs for an RNN-based model, existing approaches integrate control parameters by concatenating them channel-wisely with some intermediate representation of the input signal. While this method is parameter-efficient, there is room to further improve the quality of generated audio because the concatenation-based conditioning method has limited capacity in modulating signals. In this paper, we propose three novel conditioning mechanisms for RNNs, tailored for black-box virtual analog modeling. These advanced conditioning mechanisms modulate the model based on control parameters, yielding superior results to existing RNN- and CNN-based architectures across various evaluation metrics.