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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Lightweight and Interpretable Neural Modeling of an Audio Distortion Effect Using Hyperconditioned Differentiable Biquads
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Lightweight and Interpretable Neural Modeling of an Audio Distortion Effect Using Hyperconditioned Differentiable Biquads

机译：使用高音定义可微分的销售的轻量级和可解释的神经建模的音频失真效果

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摘要

In this work, we propose using differentiable cascaded biquads to model an audio distortion effect. We extend trainable infinite impulse response (IIR) filters to the hyperconditioned case, in which a transformation is learned to directly map external parameters of the distortion effect to its internal filter and gain parameters, along with activations necessary to ensure filter stability. We propose a novel, efficient training scheme of IIR filters by means of a Fourier transform. Our models have significantly fewer parameters and reduced complexity relative to more traditional black-box neural audio effect modeling methodologies using finite impulse response filters. Our smallest, best-performing model adequately models a BOSS MT-2 pedal at 44.1 kHz, using a total of 40 biquads and only 210 parameters. Its model parameters are interpretable, can be related back to the original analog audio circuit, and can even be intuitively altered by machine learning non-specialists after model training. Quantitative and qualitative results illustrate the effectiveness of the proposed method.
机译：在这项工作中，我们建议使用可微分的级联替代店来模拟音频失真效果。我们将培训无限脉冲响应（IIR）滤波器扩展到超级说明性情况，其中学习了转换，以直接将失真效果的外部参数映射到其内部过滤器和增益参数以及确保滤波器稳定所需的激活。我们通过傅里叶变换提出了一种新颖的，高效的IIR过滤器训练方案。我们的模型具有显着较少的参数和相对于使用有限脉冲响应过滤器更加传统的黑盒神经音频效果建模方法的复杂性。我们最小，表现最佳的模型充分展示了44.1 kHz的老板MT-2踏板，共使用40个替代，只有210个参数。其模型参数是可解释的，可以返回原始模拟音频电路，甚至可以通过机器学习非专家在模型训练后直观地改变。定量和定性结果说明了所提出的方法的有效性。

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