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Sparsity-based audio declipping methods: selected overview, new algorithms, and large-scale evaluation
- Source :
- IEEE/ACM Transactions on Audio, Speech and Language Processing, IEEE/ACM Transactions on Audio, Speech and Language Processing, 2021, 29, pp.1174-1187. ⟨10.1109/TASLP.2021.3059264⟩, IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2021, 29, pp.1174-1187. ⟨10.1109/TASLP.2021.3059264⟩
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- 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.
- Subjects :
- Acoustics and Ultrasonics
Computer science
audio declipping
02 engineering and technology
Machine learning
computer.software_genre
time-frequency
Computer Science - Sound
Field (computer science)
030507 speech-language pathology & audiology
03 medical and health sciences
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Distortion
listening test
0202 electrical engineering, electronic engineering, information engineering
Computer Science (miscellaneous)
Code (cryptography)
Electrical Engineering and Systems Science - Signal Processing
Electrical and Electronic Engineering
business.industry
sparsity
020206 networking & telecommunications
structured sparsity
Benchmarking
Speech processing
Computational Mathematics
[INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD]
Benchmark (computing)
Artificial intelligence
0305 other medical science
Clipping (computer graphics)
business
Scale (map)
computer
Electrical Engineering and Systems Science - Audio and Speech Processing
Subjects
Details
- Language :
- English
- ISSN :
- 23299290 and 23299304
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM Transactions on Audio, Speech and Language Processing, IEEE/ACM Transactions on Audio, Speech and Language Processing, 2021, 29, pp.1174-1187. ⟨10.1109/TASLP.2021.3059264⟩, IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2021, 29, pp.1174-1187. ⟨10.1109/TASLP.2021.3059264⟩
- Accession number :
- edsair.doi.dedup.....4043939bec429a19757a204d4264938d