تقرير
Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling
| العنوان: | Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling |
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| المؤلفون: | Yeh, Yen-Tung, Hsiao, Wen-Yi, Yang, Yi-Hsuan |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | 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. Comment: Accepted to DAFx24 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.04829 |
| رقم الأكسشن: | edsarx.2408.04829 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |