2020/05/08 by Clément Gaultier, Gaultier, Clément, Srđan Kitić +5
Computer Science · Engineering · #Acoustic Wave Phenomena Research #Advanced Adaptive Filtering Techniques #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Signal Processing (eess.SP) #Sound (cs.SD) #Speech and Audio Processing #cs.SD #eess.AS #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2005.10228
openalex publication_date 2020/05/19 · arxiv created 2020/11/30 · arxiv updated 2020/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.