Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917744449945600 |
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| author | Yeh, Yen-Tung Hsiao, Wen-Yi Yang, Yi-Hsuan |
| author_facet | Yeh, Yen-Tung Hsiao, Wen-Yi Yang, Yi-Hsuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04829 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling Yeh, Yen-Tung Hsiao, Wen-Yi Yang, Yi-Hsuan Sound Audio and Speech Processing 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. |
| title | Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2408.04829 |