论文标题
Applate：可调节的插件音频声明器将DNN与稀疏优化相结合
APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with Sparse Optimization
论文作者
论文摘要
在本文中，我们提出了一种音频倾斜的方法，该方法既利用稀疏优化和深度学习的优势。由于基于稀疏性的音频倾倒方法是在受限优化的情况下开发的，因此它们在理论上是可调且研究的。但是，它们总是统一地促进稀疏性并忽略信号的个别特性。基于深的神经网络（DNN）方法可以学习目标信号的属性，并将其用于音频下降。尽管如此，如果训练数据在时间域中没有施加不匹配和/或约束，它们的表现就无法表现良好。在提出的方法中，我们在优化算法中使用DNN。它的灵感来自一个称为插件（PNP）的想法，并使我们能够根据数据的限制在时域中的限制来促进稀疏性。我们的实验证实，该提出的方法对于训练和测试数据之间的不匹配是稳定且鲁棒的。
In this paper, we propose an audio declipping method that takes advantages of both sparse optimization and deep learning. Since sparsity-based audio declipping methods have been developed upon constrained optimization, they are adjustable and well-studied in theory. However, they always uniformly promote sparsity and ignore the individual properties of a signal. Deep neural network (DNN)-based methods can learn the properties of target signals and use them for audio declipping. Still, they cannot perform well if the training data have mismatches and/or constraints in the time domain are not imposed. In the proposed method, we use a DNN in an optimization algorithm. It is inspired by an idea called plug-and-play (PnP) and enables us to promote sparsity based on the learned information of data, considering constraints in the time domain. Our experiments confirmed that the proposed method is stable and robust to mismatches between training and test data.
