论文标题
柔软的平滑度，用于使用延迟式空间中的潜在矩阵模型来介绍音频的音频
Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space
论文作者
论文摘要
在这里，我们提出了一种新的平滑时间序列信号重建方法。这项研究的一个关键概念不是在信号空间中考虑该模型，而是在延迟被填充的空间中考虑模型。换句话说，我们间接表示一个时间序列信号作为矩阵的反向延迟变为的输出，并且矩阵受到约束。基于反向延迟插入的模型，我们建议将矩阵限制为具有光滑因子向量的等级-1。所提出的模型与卷积模型和二次变化（QV）正则化密切相关。特别是，所提出的方法可以表征为QV正则化的概括。此外，我们表明所提出的方法比QV正则化提供了柔和的平滑度。与几种现有的插值方法和稀疏建模相比，进行了音频填充和倾斜的实验以显示其优势。
Here, we propose a new reconstruction method of smooth time-series signals. A key concept of this study is not considering the model in signal space, but in delay-embedded space. In other words, we indirectly represent a time-series signal as an output of inverse delay-embedding of a matrix, and the matrix is constrained. Based on the model under inverse delay-embedding, we propose to constrain the matrix to be rank-1 with smooth factor vectors. The proposed model is closely related to the convolutional model, and quadratic variation (QV) regularization. Especially, the proposed method can be characterized as a generalization of QV regularization. In addition, we show that the proposed method provides the softer smoothness than QV regularization. Experiments of audio inpainting and declipping are conducted to show its advantages in comparison with several existing interpolation methods and sparse modeling.
