We deal with the problem of sparsity-based audio inpainting, i.e. filling in the missing segments of audio. A consequence of the approaches based on mathematical optimization is the insufficient amplitude of the signal in the filled gaps. Remaining in the framework based on sparsity and convex optimization, we propose improvements to audio inpainting, aiming at compensating for such an energy loss. The new ideas are based on different types of weighting, both in the coefficient and the time domains. We show that our propositions improve the inpainting performance in terms of both the SNR and ODG.

中文翻译：

---

音频修复：重新审视和重新加权

我们处理基于稀疏性的音频修复问题，即填充丢失的音频片段。基于数学优化的方法的结果是填充间隙中信号的幅度不足。保留在基于稀疏和凸优化的框架中，我们提出了音频修复的改进，旨在补偿这种能量损失。新想法基于不同类型的加权，包括系数和时域。我们表明，我们的提议在 SNR 和 ODG 方面都提高了修复性能。