Abstract
The Euclidean distance between wavelet scattering transform coefficients (known as paths) provides informative gradients for perceptual quality assessment of deep inverse problems in computer vision, speech, and audio processing. However, these transforms are computationally expensive when employed as differentiable loss functions for stochastic gradient descent due to their numerous paths, which significantly limits their use in neural network training. Against this problem, we propose "Scattering transform with Random Paths for machine Learning" (SCRAPL): a stochastic optimization scheme for efficient evaluation of multivariable scattering transforms. We implement SCRAPL for the joint time-frequency scattering transform (JTFS) which demodulates spectrotemporal patterns at multiple scales and rates, allowing a fine characterization of intermittent auditory textures. We apply SCRAPL to differentiable digital signal processing (DDSP), specifically, unsupervised sound matching of a granular synthesizer and the Roland TR-808 drum machine. We also propose an initialization heuristic based on importance sampling, which adapts SCRAPL to the perceptual content of the dataset, improving neural network convergence and evaluation performance. We make our code and audio samples available and provide SCRAPL as a Python package.
Abstract (translated)
波lets变换系数（也称为路径）之间的欧几里得距离为计算机视觉、语音和音频处理中的深度逆问题的感知质量评估提供了有信息量的梯度。然而，由于这些变换具有许多路径，在用作随机梯度下降（SGD）的可微损失函数时计算成本高昂，这极大地限制了它们在神经网络训练中的应用。为了解决这个问题，我们提出了“机器学习中带有随机路径的散射变换”(SCRAPL)：一种高效的多变量散射变换评估的随机优化方案。我们在联合时频散射变换(JTFS)上实现了SCRAPL，该变换可以在多个尺度和速率下解调频谱-时间模式，并允许对间歇性听觉纹理进行精细表征。我们将SCRAPL应用于可微数字信号处理（DDSP），特别是用于颗粒合成器与Roland TR-808鼓机之间的无监督声音匹配。我们还提出了一种基于重要性采样的初始化启发式方法，该方法将SCRAPL适应于数据集的感知内容，从而提高神经网络的收敛性和评估性能。我们将代码、音频样本以及SCRAPL作为Python包提供给社区使用。
URL
https://arxiv.org/abs/2602.11145
