用于模拟动态范围压缩实时建模的高效神经网络
Efficient neural networks for real-time modeling of analog dynamic range compression
摘要：
深度学习方法在模拟音频效果建模方面取得了成功。然而，在对涉及时变非线性元件（例如动态范围压缩器）的更复杂的效应进行建模方面仍然存在挑战。现有的神经网络建模压缩方法要么忽略设备参数，不达到足够的精度，否则需要大型非因果模型，禁止实时操作。在这项工作中，我们提出了对时间卷积网络（TCN）的修改，在不牺牲性能的情况下提高效率。通过利用非常稀疏的卷积核通过快速增长的膨胀，我们的模型使用更少的层获得了显着的感受野，减少了计算。通过详细的评估，我们证明了我们的高效和因果关系的方法在模拟LA-2A建模方面达到了最先进的性能，能够在CPU上实时操作，并且只需要10分钟的训练数据。
英文摘要：
Deep learning approaches have demonstrated success in modeling analog audio effects. Nevertheless, challenges remain in modeling more complex effects that involve time-varying nonlinear elements, such as dynamic range compressors. Existing neural network approaches for modeling compression either ignore the device parameters, do not attain sufficient accuracy, or otherwise require large noncausal models prohibiting real-time operation. In this work, we propose a modification to temporal convolutional networks (TCNs) enabling greater efficiency without sacrificing performance. By utilizing very sparse convolutional kernels through rapidly growing dilations, our model attains a significant receptive field using fewer layers, reducing computation. Through a detailed evaluation we demonstrate our efficient and causal approach achieves state-of-the-art performance in modeling the analog LA-2A, is capable of real-time operation on CPU, and only requires 10 minutes of training data.
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