论文标题
通过$ \ ell_1 $ - 最大化和词典学习的音频插图
Audio Inpainting via $\ell_1$-Minimization and Dictionary Learning
论文作者
论文摘要
音频介入是指旨在恢复音频信号中缺失或损坏的连续样本的信号处理技术。先前的工作表明，适当加权的$ \ ell_1 $ - 最小化能够解决分析和合成模型的音频介入问题。这些模型假定音频信号相对于某些冗余词典而言是稀疏的，并且利用这种稀疏性是为了涂上目的。在稀疏框架内，我们利用词典学习来进一步增加稀疏性，并将其与加权$ \ ell_1 $ - 毫米化相结合，适合于音频介绍，以补偿恢复后间隙内的能量损失。我们的实验表明，与原始对应物相比，我们的方法在信噪比（SDR）和客观差等级（ODG）方面取得了优越。
Audio inpainting refers to signal processing techniques that aim at restoring missing or corrupted consecutive samples in audio signals. Prior works have shown that $\ell_1$- minimization with appropriate weighting is capable of solving audio inpainting problems, both for the analysis and the synthesis models. These models assume that audio signals are sparse with respect to some redundant dictionary and exploit that sparsity for inpainting purposes. Remaining within the sparsity framework, we utilize dictionary learning to further increase the sparsity and combine it with weighted $\ell_1$-minimization adapted for audio inpainting to compensate for the loss of energy within the gap after restoration. Our experiments demonstrate that our approach is superior in terms of signal-to-distortion ratio (SDR) and objective difference grade (ODG) compared with its original counterpart.
