The objective of audio inpainting is to fill a gap in an audio signal. This is ideally done by reconstructing the original signal or, at least, by inferring a meaningful surrogate signal. We propose a novel approach applying sparse modeling in the time-frequency (TF) domain. In particular, we devise a dictionary learning technique which learns the dictionary from reliable parts around the gap with the goal to obtain a signal representation with increased TF sparsity. This is based on a basis optimization technique to deform a given Gabor frame such that the sparsity of the analysis coefficients of the resulting frame is maximized. Furthermore, we modify the SParse Audio INpainter (SPAIN) for both the analysis and the synthesis model such that it is able to exploit the increased TF sparsity and—in turn—benefits from dictionary learning. Our experiments demonstrate that the developed methods achieve significant gains in terms of signal-to-distortion ratio (SDR) and objective difference grade (ODG) compared with several state-of-the-art audio inpainting techniques.

中文翻译：

---

字典学习稀疏音频修复

音频修复的目的是填补音频信号中的空白。理想地，这是通过重建原始信号或至少通过推断有意义的代理信号来完成的。我们提出了一种在时频（TF）域中应用稀疏建模的新颖方法。特别地，我们设计了一种字典学习技术，该技术从间隙周围的可靠部分学习字典，目的是获得具有更高TF稀疏性的信号表示。这是基于使给定Gabor框架变形的基本优化技术，从而使所得框架的分析系数的稀疏性最大化。此外，我们针对分析模型和综合模型修改了SParse Audio INpainter（SPAIN），以使其能够利用增加的TF稀疏性，进而从字典学习中受益。