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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.
Clipping is a non-linear signal distortion usually appearing when a signal exceeds its allowed dynamic range. As a typical instance, an analog signal that is digitized can be clipped in value when its original peak values go beyond the largest (or lowest) digit representation. For this reason, the effect is also called saturation.
Clipping in audio signals has a great negative effect on the perceptual quality of audio , and it reduces the accuracy of automatic speech recognition ,  and other audio analysis applications. To improve the perceived quality of audio, a recovery of clipped samples can be made; this process is usually termed declipping.