Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling
<|reference_start|>Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling: 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.<|reference_end|>
@article{yeh2024hyper,
    title={Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio
  Effect Modeling},
    author={Yen-Tung Yeh, Wen-Yi Hsiao, Yi-Hsuan Yang},
    journal={arXiv preprint arXiv:2408.04829},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04829},
    primaryClass={cs.SD eess.AS}
}