Sparsity-based audio declipping methods: selected overview, new algorithms, and large-scale evaluation
This work addresses the need for practical guidelines in audio declipping for practitioners, though it is incremental as it builds on existing sparsity-based methods.
The authors tackled the problem of selecting audio declipping methods by proposing a general algorithmic framework and conducting large-scale evaluations to provide guidelines for different clipping levels and audio types, resulting in publicly available code for reproducible research.
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.