  Recent advances in audio declipping have substantially improved the state of
the art.% in certain saturation regimes. Yet, practitioners need guidelines to
choose a method, and while existing benchmarks have been instrumental in
advancing the field, larger-scale experiments are needed to guide such choices.
First, we show that the clipping levels in existing small-scale benchmarks are
moderate and call for benchmarks with more perceptually significant clipping
levels. We then propose a general algorithmic framework for declipping that
covers existing and new combinations of variants of state-of-the-art techniques
exploiting time-frequency sparsity: synthesis vs. analysis sparsity, with plain
or structured sparsity. Finally, we systematically compare these combinations
and a selection of state-of-the-art methods. Using a large-scale numerical
benchmark and a smaller scale formal listening test, we provide guidelines for
various clipping levels, both for speech and various musical genres. The code
is made publicly available for the purpose of reproducible research and
benchmarking.
