  Dynamic range limitations in signal processing often lead to clipping, or
saturation, in signals. The task of audio declipping is estimating the original
audio signal, given its clipped measurements, and has attracted much interest
in recent years. Audio declipping algorithms often make assumptions about the
underlying signal, such as sparsity or low-rankness, and about the measurement
system. In this paper, we provide an extensive review of audio declipping
algorithms proposed in the literature. For each algorithm, we present
assumptions that are made about the audio signal, the modeling domain, and 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, and also using
perceptual metrics of sound quality. The article is accompanied by a repository
containing the evaluated methods.
