Dynamic range limitations in signal processing often lead to clipping, or saturation, in signals. Audio declipping is the task of estimating the original audio signal given its clipped measurements and has attracted a lot of interest in recent years. Audio declipping algorithms often make assumptions about the underlying signal, such as sparsity or low-rankness, as well as the measurement system. In this paper, we provide an extensive review of audio declipping algorithms proposed in the literature. For each algorithm, we present the assumptions being made about the audio signal, the modeling domain, as well as the optimization algorithm. Furthermore, we provide an extensive numerical evaluation of popular declipping algorithms, on real audio data. We evaluate each algorithm in terms of the Signal-to-Distortion Ratio, as well as using perceptual metrics of sound quality. The article is accompanied with the repository containing the evaluated methods.

中文翻译：

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对流行的音频剪辑方法的调查和广泛评估

信号处理中的动态范围限制通常会导致信号削波或饱和。音频去限幅是在给定其限幅测量值的情况下估计原始音频信号的任务，近年来引起了很多兴趣。音频去剪算法通常对底层信号以及测量系统做出假设，例如稀疏性或低秩。在本文中，我们对文献中提出的音频去剪算法进行了广泛的回顾。对于每种算法，我们都提出了关于音频信号、建模域以及优化算法的假设。此外，我们对真实音频数据的流行去剪算法进行了广泛的数值评估。我们根据信号失真比评估每个算法，以及使用声音质量的感知指标。该文章随附包含评估方法的存储库。